Hugging Face's logo

Datasets:
General-Level
/
General-Bench-Closeset-Scoped

ArXiv:
Dataset card Files
xet
Community
1
wushengqiong commited on
Commit
25728e7
·
1 Parent(s): fa15ad2

update

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/accessory/annotation.json +258 -0
  2. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/appliance/annotation.json +258 -0
  3. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/electronic/annotation.json +258 -0
  4. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/furniture/annotation.json +162 -0
  5. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/musical_instrument/annotation.json +258 -0
  6. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/person/annotation.json +162 -0
  7. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/structure/annotation.json +258 -0
  8. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/tableware/annotation.json +258 -0
  9. Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/vehicle/annotation.json +258 -0
  10. Scope-A/S-A-Quick/3D/comprehension/3d_indoor_semantic_segmentation/scannet/annotation.json +318 -0
  11. Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_instance_segmentation/semantic_kitti/annotation.json +238 -0
  12. Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_odometry/kitti_odometry/annotation.json +84 -0
  13. Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/annotation.json +238 -0
  14. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/aircrafts/annotation.json +258 -0
  15. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/furniture/annotation.json +258 -0
  16. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/personal_item/annotation.json +258 -0
  17. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/tableware/annotation.json +258 -0
  18. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/vehicles/annotation.json +258 -0
  19. Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/weapons/annotation.json +258 -0
  20. Scope-A/S-A-Quick/3D/generation/3d_point_cloud_completion/annotation.json +119 -0
  21. Scope-A/S-A-Quick/3D/generation/rgbd_to_mesh/scannet/annotation_v1.json +127 -0
  22. Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/culture_and_structure_closeset.json +107 -0
  23. Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/living_and_art_closeset.json +107 -0
  24. Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/nature_and_biology_closeset.json +107 -0
  25. Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/science_and_technology_closeset.json +107 -0
  26. Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/culture_and_structure.json +107 -0
  27. Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/living_and_art.json +107 -0
  28. Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/nature_and_biology.json +107 -0
  29. Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/science_and_technology.json +107 -0
  30. Scope-A/S-A-Quick/audio/comprehension/AccentClassification/annotation.json +256 -0
  31. Scope-A/S-A-Quick/audio/comprehension/AccentSexClassification/annotation.json +256 -0
  32. Scope-A/S-A-Quick/audio/comprehension/AcousticSceneRecognition/annotation.json +137 -0
  33. Scope-A/S-A-Quick/audio/comprehension/AnimalSoundDetection/annotation.json +177 -0
  34. Scope-A/S-A-Quick/audio/comprehension/AudioQuestionAnswering/annotation.json +256 -0
  35. Scope-A/S-A-Quick/audio/comprehension/BirdSoundDetection/annotation.json +180 -0
  36. Scope-A/S-A-Quick/audio/comprehension/EnvironmentSoundRecognition/annotation.json +136 -0
  37. Scope-A/S-A-Quick/audio/comprehension/IntentClassification/annotation.json +233 -0
  38. Scope-A/S-A-Quick/audio/comprehension/LongAudioCaptioning/annotation.json +416 -0
  39. Scope-A/S-A-Quick/audio/comprehension/MusicGenreClassification/annotation.json +136 -0
  40. Scope-A/S-A-Quick/audio/comprehension/MusicInstrumentClassification/annotation.json +136 -0
  41. Scope-A/S-A-Quick/audio/comprehension/MusicInstrumentSourceAnalysis/annotation.json +136 -0
  42. Scope-A/S-A-Quick/audio/comprehension/MusicPitchAnalysis/annotation.json +136 -0
  43. Scope-A/S-A-Quick/audio/comprehension/NoteQualitiesAnalysis/annotation.json +176 -0
  44. Scope-A/S-A-Quick/audio/comprehension/OpenAudioQuestionAnswering/annotation.json +256 -0
  45. Scope-A/S-A-Quick/audio/comprehension/SingerIdentification/annotation.json +177 -0
  46. Scope-A/S-A-Quick/audio/comprehension/SoundEventRecognition/annotation.json +136 -0
  47. Scope-A/S-A-Quick/audio/comprehension/SpeakerIdentification/annotation.json +257 -0
  48. Scope-A/S-A-Quick/audio/comprehension/SpeechCommand/annotation.json +176 -0
  49. Scope-A/S-A-Quick/audio/comprehension/SpeechEmotionRecognition/annotation.json +263 -0
  50. Scope-A/S-A-Quick/audio/comprehension/SpeechEventExtraction/annotation.json +177 -0
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/accessory/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0221.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0147.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0147"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0332.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0105.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0105"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/vase/test/vase_0513.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0016.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0016"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0177.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0170.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0170"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0329.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0086.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0086"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0344.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0026.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0026"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0232.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0032.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0032"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0319.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0067.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0067"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/vase/test/vase_0570.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0157.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0157"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0348.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0166.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0166"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/vase/test/vase_0480.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0139.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0139"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0372.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0112.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0112"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0261.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0110.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0110"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0297.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0031.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0031"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/vase/test/vase_0509.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0011.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0011"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0176.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0191.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0191"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/vase/test/vase_0544.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0154.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0154"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/glass_box/test/glass_box_0239.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0127.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0127"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/flower_pot/test/flower_pot_0160.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0003.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0003"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/mantel/test/mantel_0340.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/accessory/3d_points/0162.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0162"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/appliance/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0371.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0009.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0009"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0165.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0012.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0012"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0361.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0068.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0068"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0346.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0045.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0045"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0132.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0103.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0103"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0192.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0074.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0074"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0135.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0130.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0130"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0180.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0015.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0015"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0373.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0143.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0143"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0141.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0115.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0115"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0354.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0124.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0124"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0400.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0101.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0101"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0194.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0110.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0110"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0379.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0002.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0002"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0151.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0087.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0087"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0440.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0049.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0049"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0390.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0122.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0122"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/toilet/test/toilet_0425.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0033.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0033"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/range_hood/test/range_hood_0121.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0022.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0022"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/lamp/test/lamp_0134.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/appliance/3d_points/0007.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0007"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/electronic/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0478.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0074.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0074"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0492.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0106.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0106"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/xbox/test/xbox_0121.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0046.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0046"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0498.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0045.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0045"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0474.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0071.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0071"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/radio/test/radio_0113.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0090.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0090"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/xbox/test/xbox_0104.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0092.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0092"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0469.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0020.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0020"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0480.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0055.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0055"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/radio/test/radio_0114.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0015.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0015"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/radio/test/radio_0119.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0060.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0060"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0485.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0075.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0075"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0555.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0043.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0043"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/xbox/test/xbox_0112.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0072.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0072"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0522.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0042.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0042"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0516.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0051.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0051"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/keyboard/test/keyboard_0148.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0065.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0065"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0519.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0053.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0053"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0524.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0010.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0010"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/monitor/test/monitor_0550.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/electronic/3d_points/0032.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0032"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/furniture/annotation.json ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/dresser/test/dresser_0262.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0038.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0038"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/tv_stand/test/tv_stand_0347.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0248.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0248"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/bathtub/test/bathtub_0147.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0196.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0196"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/tv_stand/test/tv_stand_0341.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0280.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0280"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/dresser/test/dresser_0277.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0005.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0005"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/sofa/test/sofa_0734.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0051.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0051"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/chair/test/chair_0979.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0162.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0162"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/tv_stand/test/tv_stand_0355.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0245.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0245"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/dresser/test/dresser_0242.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0229.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0229"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/chair/test/chair_0916.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0233.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0233"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/bookshelf/test/bookshelf_0606.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0176.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0176"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/bathtub/test/bathtub_0130.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/furniture/3d_points/0092.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0092"
160
+ }
161
+ ]
162
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/musical_instrument/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0249.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0067.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0067"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0161.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0019.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0019"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0207.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0035.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0035"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0200.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0048.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0048"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0240.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0034.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0034"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/piano/test/piano_0328.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0062.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0062"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0237.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0093.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0093"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0174.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0075.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0075"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0157.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0051.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0051"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0233.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0027.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0027"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/piano/test/piano_0262.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0115.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0115"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/piano/test/piano_0258.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0004.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0004"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0191.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0071.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0071"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0254.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0016.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0016"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0164.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0107.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0107"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0170.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0097.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0097"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0186.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0046.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0046"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/piano/test/piano_0289.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0040.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0040"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0159.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0006.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0006"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/guitar/test/guitar_0217.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/musical_instrument/3d_points/0031.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0031"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/person/annotation.json ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0092.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0005.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0005"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0093.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0001.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0001"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0105.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0002.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0002"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0098.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0010.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0010"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0094.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0003.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0003"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0099.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0000.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0000"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0089.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0004.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0004"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0100.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0006.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0006"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0095.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0007.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0007"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0096.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0009.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0009"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0107.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0011.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0011"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/person/test/person_0102.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/person/3d_points/0008.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0008"
160
+ }
161
+ ]
162
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/structure/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0144.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0008.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0008"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0131.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0004.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0004"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0140.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0003.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0003"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0177.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0007.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0007"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0175.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0018.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0018"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0129.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0001.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0001"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0169.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0009.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0009"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0167.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0014.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0014"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0132.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0022.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0022"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0172.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0023.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0023"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0168.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0005.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0005"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0134.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0020.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0020"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0176.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0012.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0012"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0133.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0006.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0006"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0128.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0000.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0000"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0135.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0017.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0017"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0166.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0015.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0015"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0171.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0002.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0002"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/tent/test/tent_0179.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0013.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0013"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/stairs/test/stairs_0143.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/structure/3d_points/0016.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0016"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/tableware/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0349.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0008.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0008"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0373.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0073.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0073"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/cup/test/cup_0095.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0047.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0047"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/cup/test/cup_0082.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0015.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0015"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0342.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0014.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0014"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/cup/test/cup_0084.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0026.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0026"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0389.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0032.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0032"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0341.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0020.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0020"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0370.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0036.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0036"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0368.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0066.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0066"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0397.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0070.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0070"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0423.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0007.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0007"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/cup/test/cup_0083.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0074.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0074"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0359.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0017.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0017"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/cup/test/cup_0086.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0050.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0050"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0411.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0029.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0029"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0380.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0053.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0053"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/bowl/test/bowl_0083.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0030.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0030"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0391.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0060.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0060"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/bottle/test/bottle_0429.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/tableware/3d_points/0042.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0042"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_classification/ModelNet40/vehicle/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_classification",
3
+ "data_source": "ModelNet40",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0271.off",
20
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0047.off",
21
+ "question": "classify the 3d object",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0047"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0252.off",
32
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0075.off",
33
+ "question": "classify the 3d object",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0075"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0656.off",
44
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0098.off",
45
+ "question": "classify the 3d object",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0098"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0700.off",
56
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0097.off",
57
+ "question": "classify the 3d object",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0097"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0662.off",
68
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0038.off",
69
+ "question": "classify the 3d object",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0038"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0263.off",
80
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0074.off",
81
+ "question": "classify the 3d object",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0074"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0289.off",
92
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0070.off",
93
+ "question": "classify the 3d object",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0070"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0214.off",
104
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0030.off",
105
+ "question": "classify the 3d object",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0030"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0275.off",
116
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0103.off",
117
+ "question": "classify the 3d object",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0103"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0704.off",
128
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0017.off",
129
+ "question": "classify the 3d object",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0017"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0207.off",
140
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0118.off",
141
+ "question": "classify the 3d object",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0118"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0692.off",
152
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0078.off",
153
+ "question": "classify the 3d object",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0078"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0276.off",
164
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0060.off",
165
+ "question": "classify the 3d object",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0060"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0251.off",
176
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0107.off",
177
+ "question": "classify the 3d object",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0107"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0664.off",
188
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0077.off",
189
+ "question": "classify the 3d object",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0077"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0257.off",
200
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0089.off",
201
+ "question": "classify the 3d object",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0089"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0718.off",
212
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0005.off",
213
+ "question": "classify the 3d object",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0005"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0204.off",
224
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0016.off",
225
+ "question": "classify the 3d object",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0016"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "ModelNet/ModelNet40/car/test/car_0256.off",
236
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0100.off",
237
+ "question": "classify the 3d object",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0100"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "ModelNet/ModelNet40/airplane/test/airplane_0680.off",
248
+ "3d_point_file": "comprehension/3d_classification/ModelNet40/vehicle/3d_points/0022.off",
249
+ "question": "classify the 3d object",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0022"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_indoor_semantic_segmentation/scannet/annotation.json ADDED
@@ -0,0 +1,318 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_semantic_segmentation",
3
+ "data_source": "scannet",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0412_00/scene0412_00_vh_clean_2.ply",
20
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0412_00/scene0412_00.sens",
21
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0412_00/scene0412_00_vh_clean_2.0.010000.segs.json",
22
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0412_00/scene0412_00_vh_clean.aggregation.json",
23
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0412_00/scene0412_00.txt",
24
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
25
+ "prompt": "users' specifically-defined prompts for different models"
26
+ },
27
+ "output": {
28
+ "answer": ""
29
+ },
30
+ "id": "0037"
31
+ },
32
+ {
33
+ "input": {
34
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0474_00/scene0474_00_vh_clean_2.ply",
35
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0474_00/scene0474_00.sens",
36
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0474_00/scene0474_00_vh_clean_2.0.010000.segs.json",
37
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0474_00/scene0474_00_vh_clean.aggregation.json",
38
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0474_00/scene0474_00.txt",
39
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
40
+ "prompt": "users' specifically-defined prompts for different models"
41
+ },
42
+ "output": {
43
+ "answer": ""
44
+ },
45
+ "id": "0055"
46
+ },
47
+ {
48
+ "input": {
49
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0430_00/scene0430_00_vh_clean_2.ply",
50
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0430_00/scene0430_00.sens",
51
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0430_00/scene0430_00_vh_clean_2.0.010000.segs.json",
52
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0430_00/scene0430_00_vh_clean.aggregation.json",
53
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0430_00/scene0430_00.txt",
54
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
55
+ "prompt": "users' specifically-defined prompts for different models"
56
+ },
57
+ "output": {
58
+ "answer": ""
59
+ },
60
+ "id": "0066"
61
+ },
62
+ {
63
+ "input": {
64
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0550_00/scene0550_00_vh_clean_2.ply",
65
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0550_00/scene0550_00.sens",
66
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0550_00/scene0550_00_vh_clean_2.0.010000.segs.json",
67
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0550_00/scene0550_00_vh_clean.aggregation.json",
68
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0550_00/scene0550_00.txt",
69
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0027"
76
+ },
77
+ {
78
+ "input": {
79
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0633_00/scene0633_00_vh_clean_2.ply",
80
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0633_00/scene0633_00.sens",
81
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0633_00/scene0633_00_vh_clean_2.0.010000.segs.json",
82
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0633_00/scene0633_00_vh_clean.aggregation.json",
83
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0633_00/scene0633_00.txt",
84
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
85
+ "prompt": "users' specifically-defined prompts for different models"
86
+ },
87
+ "output": {
88
+ "answer": ""
89
+ },
90
+ "id": "0082"
91
+ },
92
+ {
93
+ "input": {
94
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0500_00/scene0500_00_vh_clean_2.ply",
95
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0500_00/scene0500_00.sens",
96
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0500_00/scene0500_00_vh_clean_2.0.010000.segs.json",
97
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0500_00/scene0500_00_vh_clean.aggregation.json",
98
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0500_00/scene0500_00.txt",
99
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
100
+ "prompt": "users' specifically-defined prompts for different models"
101
+ },
102
+ "output": {
103
+ "answer": ""
104
+ },
105
+ "id": "0057"
106
+ },
107
+ {
108
+ "input": {
109
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0153_00/scene0153_00_vh_clean_2.ply",
110
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0153_00/scene0153_00.sens",
111
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0153_00/scene0153_00_vh_clean_2.0.010000.segs.json",
112
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0153_00/scene0153_00_vh_clean.aggregation.json",
113
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0153_00/scene0153_00.txt",
114
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
115
+ "prompt": "users' specifically-defined prompts for different models"
116
+ },
117
+ "output": {
118
+ "answer": ""
119
+ },
120
+ "id": "0017"
121
+ },
122
+ {
123
+ "input": {
124
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0701_00/scene0701_00_vh_clean_2.ply",
125
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0701_00/scene0701_00.sens",
126
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0701_00/scene0701_00_vh_clean_2.0.010000.segs.json",
127
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0701_00/scene0701_00_vh_clean.aggregation.json",
128
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0701_00/scene0701_00.txt",
129
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0052"
136
+ },
137
+ {
138
+ "input": {
139
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0328_00/scene0328_00_vh_clean_2.ply",
140
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0328_00/scene0328_00.sens",
141
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0328_00/scene0328_00_vh_clean_2.0.010000.segs.json",
142
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0328_00/scene0328_00_vh_clean.aggregation.json",
143
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0328_00/scene0328_00.txt",
144
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
145
+ "prompt": "users' specifically-defined prompts for different models"
146
+ },
147
+ "output": {
148
+ "answer": ""
149
+ },
150
+ "id": "0058"
151
+ },
152
+ {
153
+ "input": {
154
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0086_00/scene0086_00_vh_clean_2.ply",
155
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0086_00/scene0086_00.sens",
156
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0086_00/scene0086_00_vh_clean_2.0.010000.segs.json",
157
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0086_00/scene0086_00_vh_clean.aggregation.json",
158
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0086_00/scene0086_00.txt",
159
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
160
+ "prompt": "users' specifically-defined prompts for different models"
161
+ },
162
+ "output": {
163
+ "answer": ""
164
+ },
165
+ "id": "0044"
166
+ },
167
+ {
168
+ "input": {
169
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0644_00/scene0644_00_vh_clean_2.ply",
170
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0644_00/scene0644_00.sens",
171
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0644_00/scene0644_00_vh_clean_2.0.010000.segs.json",
172
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0644_00/scene0644_00_vh_clean.aggregation.json",
173
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0644_00/scene0644_00.txt",
174
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
175
+ "prompt": "users' specifically-defined prompts for different models"
176
+ },
177
+ "output": {
178
+ "answer": ""
179
+ },
180
+ "id": "0026"
181
+ },
182
+ {
183
+ "input": {
184
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0618_00/scene0618_00_vh_clean_2.ply",
185
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0618_00/scene0618_00.sens",
186
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0618_00/scene0618_00_vh_clean_2.0.010000.segs.json",
187
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0618_00/scene0618_00_vh_clean.aggregation.json",
188
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0618_00/scene0618_00.txt",
189
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0040"
196
+ },
197
+ {
198
+ "input": {
199
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0490_00/scene0490_00_vh_clean_2.ply",
200
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0490_00/scene0490_00.sens",
201
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0490_00/scene0490_00_vh_clean_2.0.010000.segs.json",
202
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0490_00/scene0490_00_vh_clean.aggregation.json",
203
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0490_00/scene0490_00.txt",
204
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
205
+ "prompt": "users' specifically-defined prompts for different models"
206
+ },
207
+ "output": {
208
+ "answer": ""
209
+ },
210
+ "id": "0038"
211
+ },
212
+ {
213
+ "input": {
214
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0663_00/scene0663_00_vh_clean_2.ply",
215
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0663_00/scene0663_00.sens",
216
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0663_00/scene0663_00_vh_clean_2.0.010000.segs.json",
217
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0663_00/scene0663_00_vh_clean.aggregation.json",
218
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0663_00/scene0663_00.txt",
219
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
220
+ "prompt": "users' specifically-defined prompts for different models"
221
+ },
222
+ "output": {
223
+ "answer": ""
224
+ },
225
+ "id": "0068"
226
+ },
227
+ {
228
+ "input": {
229
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0088_00/scene0088_00_vh_clean_2.ply",
230
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0088_00/scene0088_00.sens",
231
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0088_00/scene0088_00_vh_clean_2.0.010000.segs.json",
232
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0088_00/scene0088_00_vh_clean.aggregation.json",
233
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0088_00/scene0088_00.txt",
234
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
235
+ "prompt": "users' specifically-defined prompts for different models"
236
+ },
237
+ "output": {
238
+ "answer": ""
239
+ },
240
+ "id": "0064"
241
+ },
242
+ {
243
+ "input": {
244
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0559_00/scene0559_00_vh_clean_2.ply",
245
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0559_00/scene0559_00.sens",
246
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0559_00/scene0559_00_vh_clean_2.0.010000.segs.json",
247
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0559_00/scene0559_00_vh_clean.aggregation.json",
248
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0559_00/scene0559_00.txt",
249
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0079"
256
+ },
257
+ {
258
+ "input": {
259
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0702_00/scene0702_00_vh_clean_2.ply",
260
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0702_00/scene0702_00.sens",
261
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0702_00/scene0702_00_vh_clean_2.0.010000.segs.json",
262
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0702_00/scene0702_00_vh_clean.aggregation.json",
263
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0702_00/scene0702_00.txt",
264
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
265
+ "prompt": "users' specifically-defined prompts for different models"
266
+ },
267
+ "output": {
268
+ "answer": ""
269
+ },
270
+ "id": "0075"
271
+ },
272
+ {
273
+ "input": {
274
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0144_00/scene0144_00_vh_clean_2.ply",
275
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0144_00/scene0144_00.sens",
276
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0144_00/scene0144_00_vh_clean_2.0.010000.segs.json",
277
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0144_00/scene0144_00_vh_clean.aggregation.json",
278
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0144_00/scene0144_00.txt",
279
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
280
+ "prompt": "users' specifically-defined prompts for different models"
281
+ },
282
+ "output": {
283
+ "answer": ""
284
+ },
285
+ "id": "0046"
286
+ },
287
+ {
288
+ "input": {
289
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0651_00/scene0651_00_vh_clean_2.ply",
290
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0651_00/scene0651_00.sens",
291
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0651_00/scene0651_00_vh_clean_2.0.010000.segs.json",
292
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0651_00/scene0651_00_vh_clean.aggregation.json",
293
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0651_00/scene0651_00.txt",
294
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
295
+ "prompt": "users' specifically-defined prompts for different models"
296
+ },
297
+ "output": {
298
+ "answer": ""
299
+ },
300
+ "id": "0047"
301
+ },
302
+ {
303
+ "input": {
304
+ "3d": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0131_00/scene0131_00_vh_clean_2.ply",
305
+ "rgbd": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0131_00/scene0131_00.sens",
306
+ "segs": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0131_00/scene0131_00_vh_clean_2.0.010000.segs.json",
307
+ "aggregation": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0131_00/scene0131_00_vh_clean.aggregation.json",
308
+ "txt": "comprehension/3d_indoor_semantic_segmentation/scannet/3d_points/scene0131_00/scene0131_00.txt",
309
+ "question": "Given the label_names_dict in comprehension/3d_indoor_semantic_segmentation/scannet/label_names_dict.json, segment the 3d point cloud",
310
+ "prompt": "users' specifically-defined prompts for different models"
311
+ },
312
+ "output": {
313
+ "answer": ""
314
+ },
315
+ "id": "0012"
316
+ }
317
+ ]
318
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_instance_segmentation/semantic_kitti/annotation.json ADDED
@@ -0,0 +1,238 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_instance_segmentation",
3
+ "data_source": "semantic_kitti",
4
+ "type": "instance_segmentation",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0028.bin",
20
+ "question": "segment the instances in the 3d point cloud",
21
+ "prompt": "users' specifically-defined prompts for different models"
22
+ },
23
+ "output": {
24
+ "answer": ""
25
+ },
26
+ "id": "0028"
27
+ },
28
+ {
29
+ "input": {
30
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0127.bin",
31
+ "question": "segment the instances in the 3d point cloud",
32
+ "prompt": "users' specifically-defined prompts for different models"
33
+ },
34
+ "output": {
35
+ "answer": ""
36
+ },
37
+ "id": "0127"
38
+ },
39
+ {
40
+ "input": {
41
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0105.bin",
42
+ "question": "segment the instances in the 3d point cloud",
43
+ "prompt": "users' specifically-defined prompts for different models"
44
+ },
45
+ "output": {
46
+ "answer": ""
47
+ },
48
+ "id": "0105"
49
+ },
50
+ {
51
+ "input": {
52
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0290.bin",
53
+ "question": "segment the instances in the 3d point cloud",
54
+ "prompt": "users' specifically-defined prompts for different models"
55
+ },
56
+ "output": {
57
+ "answer": ""
58
+ },
59
+ "id": "0290"
60
+ },
61
+ {
62
+ "input": {
63
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0101.bin",
64
+ "question": "segment the instances in the 3d point cloud",
65
+ "prompt": "users' specifically-defined prompts for different models"
66
+ },
67
+ "output": {
68
+ "answer": ""
69
+ },
70
+ "id": "0101"
71
+ },
72
+ {
73
+ "input": {
74
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0145.bin",
75
+ "question": "segment the instances in the 3d point cloud",
76
+ "prompt": "users' specifically-defined prompts for different models"
77
+ },
78
+ "output": {
79
+ "answer": ""
80
+ },
81
+ "id": "0145"
82
+ },
83
+ {
84
+ "input": {
85
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0221.bin",
86
+ "question": "segment the instances in the 3d point cloud",
87
+ "prompt": "users' specifically-defined prompts for different models"
88
+ },
89
+ "output": {
90
+ "answer": ""
91
+ },
92
+ "id": "0221"
93
+ },
94
+ {
95
+ "input": {
96
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0274.bin",
97
+ "question": "segment the instances in the 3d point cloud",
98
+ "prompt": "users' specifically-defined prompts for different models"
99
+ },
100
+ "output": {
101
+ "answer": ""
102
+ },
103
+ "id": "0274"
104
+ },
105
+ {
106
+ "input": {
107
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0298.bin",
108
+ "question": "segment the instances in the 3d point cloud",
109
+ "prompt": "users' specifically-defined prompts for different models"
110
+ },
111
+ "output": {
112
+ "answer": ""
113
+ },
114
+ "id": "0298"
115
+ },
116
+ {
117
+ "input": {
118
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0065.bin",
119
+ "question": "segment the instances in the 3d point cloud",
120
+ "prompt": "users' specifically-defined prompts for different models"
121
+ },
122
+ "output": {
123
+ "answer": ""
124
+ },
125
+ "id": "0065"
126
+ },
127
+ {
128
+ "input": {
129
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0159.bin",
130
+ "question": "segment the instances in the 3d point cloud",
131
+ "prompt": "users' specifically-defined prompts for different models"
132
+ },
133
+ "output": {
134
+ "answer": ""
135
+ },
136
+ "id": "0159"
137
+ },
138
+ {
139
+ "input": {
140
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0033.bin",
141
+ "question": "segment the instances in the 3d point cloud",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0033"
148
+ },
149
+ {
150
+ "input": {
151
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0268.bin",
152
+ "question": "segment the instances in the 3d point cloud",
153
+ "prompt": "users' specifically-defined prompts for different models"
154
+ },
155
+ "output": {
156
+ "answer": ""
157
+ },
158
+ "id": "0268"
159
+ },
160
+ {
161
+ "input": {
162
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0001.bin",
163
+ "question": "segment the instances in the 3d point cloud",
164
+ "prompt": "users' specifically-defined prompts for different models"
165
+ },
166
+ "output": {
167
+ "answer": ""
168
+ },
169
+ "id": "0001"
170
+ },
171
+ {
172
+ "input": {
173
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0232.bin",
174
+ "question": "segment the instances in the 3d point cloud",
175
+ "prompt": "users' specifically-defined prompts for different models"
176
+ },
177
+ "output": {
178
+ "answer": ""
179
+ },
180
+ "id": "0232"
181
+ },
182
+ {
183
+ "input": {
184
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0119.bin",
185
+ "question": "segment the instances in the 3d point cloud",
186
+ "prompt": "users' specifically-defined prompts for different models"
187
+ },
188
+ "output": {
189
+ "answer": ""
190
+ },
191
+ "id": "0119"
192
+ },
193
+ {
194
+ "input": {
195
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0058.bin",
196
+ "question": "segment the instances in the 3d point cloud",
197
+ "prompt": "users' specifically-defined prompts for different models"
198
+ },
199
+ "output": {
200
+ "answer": ""
201
+ },
202
+ "id": "0058"
203
+ },
204
+ {
205
+ "input": {
206
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0059.bin",
207
+ "question": "segment the instances in the 3d point cloud",
208
+ "prompt": "users' specifically-defined prompts for different models"
209
+ },
210
+ "output": {
211
+ "answer": ""
212
+ },
213
+ "id": "0059"
214
+ },
215
+ {
216
+ "input": {
217
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0148.bin",
218
+ "question": "segment the instances in the 3d point cloud",
219
+ "prompt": "users' specifically-defined prompts for different models"
220
+ },
221
+ "output": {
222
+ "answer": ""
223
+ },
224
+ "id": "0148"
225
+ },
226
+ {
227
+ "input": {
228
+ "3d_point_file": "comprehension/3d_outdoor_instance_segmentation/semantic_kitti/3d_points/0026.bin",
229
+ "question": "segment the instances in the 3d point cloud",
230
+ "prompt": "users' specifically-defined prompts for different models"
231
+ },
232
+ "output": {
233
+ "answer": ""
234
+ },
235
+ "id": "0026"
236
+ }
237
+ ]
238
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_odometry/kitti_odometry/annotation.json ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_outdoor_odometry",
3
+ "data_source": "kitti_odometry",
4
+ "type": "comprenhension",
5
+ "modality": {
6
+ "in": [
7
+ "ply",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/06",
20
+ "question": "provide the odometry information for the sequence",
21
+ "prompt": "users' specifically-defined prompts for different models"
22
+ },
23
+ "output": {
24
+ "answer_file": ""
25
+ },
26
+ "id": "06"
27
+ },
28
+ {
29
+ "input": {
30
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/07",
31
+ "question": "provide the odometry information for the sequence",
32
+ "prompt": "users' specifically-defined prompts for different models"
33
+ },
34
+ "output": {
35
+ "answer_file": ""
36
+ },
37
+ "id": "07"
38
+ },
39
+ {
40
+ "input": {
41
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/09",
42
+ "question": "provide the odometry information for the sequence",
43
+ "prompt": "users' specifically-defined prompts for different models"
44
+ },
45
+ "output": {
46
+ "answer_file": ""
47
+ },
48
+ "id": "09"
49
+ },
50
+ {
51
+ "input": {
52
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/10",
53
+ "question": "provide the odometry information for the sequence",
54
+ "prompt": "users' specifically-defined prompts for different models"
55
+ },
56
+ "output": {
57
+ "answer_file": ""
58
+ },
59
+ "id": "10"
60
+ },
61
+ {
62
+ "input": {
63
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/05",
64
+ "question": "provide the odometry information for the sequence",
65
+ "prompt": "users' specifically-defined prompts for different models"
66
+ },
67
+ "output": {
68
+ "answer_file": ""
69
+ },
70
+ "id": "05"
71
+ },
72
+ {
73
+ "input": {
74
+ "frames_directory": "comprehension/3d_outdoor_odometry/kitti_odometry/frames/08",
75
+ "question": "provide the odometry information for the sequence",
76
+ "prompt": "users' specifically-defined prompts for different models"
77
+ },
78
+ "output": {
79
+ "answer_file": ""
80
+ },
81
+ "id": "08"
82
+ }
83
+ ]
84
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/annotation.json ADDED
@@ -0,0 +1,238 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_semantic_segmentation",
3
+ "data_source": "semantic_kitti",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-000708.bin",
20
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
21
+ "prompt": "users' specifically-defined prompts for different models"
22
+ },
23
+ "output": {
24
+ "answer": ""
25
+ },
26
+ "id": "2213"
27
+ },
28
+ {
29
+ "input": {
30
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002068.bin",
31
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
32
+ "prompt": "users' specifically-defined prompts for different models"
33
+ },
34
+ "output": {
35
+ "answer": ""
36
+ },
37
+ "id": "1905"
38
+ },
39
+ {
40
+ "input": {
41
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002603.bin",
42
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
43
+ "prompt": "users' specifically-defined prompts for different models"
44
+ },
45
+ "output": {
46
+ "answer": ""
47
+ },
48
+ "id": "2367"
49
+ },
50
+ {
51
+ "input": {
52
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002582.bin",
53
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
54
+ "prompt": "users' specifically-defined prompts for different models"
55
+ },
56
+ "output": {
57
+ "answer": ""
58
+ },
59
+ "id": "1621"
60
+ },
61
+ {
62
+ "input": {
63
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002299.bin",
64
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
65
+ "prompt": "users' specifically-defined prompts for different models"
66
+ },
67
+ "output": {
68
+ "answer": ""
69
+ },
70
+ "id": "0157"
71
+ },
72
+ {
73
+ "input": {
74
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002168.bin",
75
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
76
+ "prompt": "users' specifically-defined prompts for different models"
77
+ },
78
+ "output": {
79
+ "answer": ""
80
+ },
81
+ "id": "0336"
82
+ },
83
+ {
84
+ "input": {
85
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001063.bin",
86
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
87
+ "prompt": "users' specifically-defined prompts for different models"
88
+ },
89
+ "output": {
90
+ "answer": ""
91
+ },
92
+ "id": "1717"
93
+ },
94
+ {
95
+ "input": {
96
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001388.bin",
97
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
98
+ "prompt": "users' specifically-defined prompts for different models"
99
+ },
100
+ "output": {
101
+ "answer": ""
102
+ },
103
+ "id": "0030"
104
+ },
105
+ {
106
+ "input": {
107
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-000612.bin",
108
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
109
+ "prompt": "users' specifically-defined prompts for different models"
110
+ },
111
+ "output": {
112
+ "answer": ""
113
+ },
114
+ "id": "2415"
115
+ },
116
+ {
117
+ "input": {
118
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002517.bin",
119
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
120
+ "prompt": "users' specifically-defined prompts for different models"
121
+ },
122
+ "output": {
123
+ "answer": ""
124
+ },
125
+ "id": "0672"
126
+ },
127
+ {
128
+ "input": {
129
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-003285.bin",
130
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
131
+ "prompt": "users' specifically-defined prompts for different models"
132
+ },
133
+ "output": {
134
+ "answer": ""
135
+ },
136
+ "id": "0116"
137
+ },
138
+ {
139
+ "input": {
140
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-003490.bin",
141
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "1628"
148
+ },
149
+ {
150
+ "input": {
151
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001911.bin",
152
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
153
+ "prompt": "users' specifically-defined prompts for different models"
154
+ },
155
+ "output": {
156
+ "answer": ""
157
+ },
158
+ "id": "2080"
159
+ },
160
+ {
161
+ "input": {
162
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001772.bin",
163
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
164
+ "prompt": "users' specifically-defined prompts for different models"
165
+ },
166
+ "output": {
167
+ "answer": ""
168
+ },
169
+ "id": "2023"
170
+ },
171
+ {
172
+ "input": {
173
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-003271.bin",
174
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
175
+ "prompt": "users' specifically-defined prompts for different models"
176
+ },
177
+ "output": {
178
+ "answer": ""
179
+ },
180
+ "id": "0470"
181
+ },
182
+ {
183
+ "input": {
184
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001253.bin",
185
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
186
+ "prompt": "users' specifically-defined prompts for different models"
187
+ },
188
+ "output": {
189
+ "answer": ""
190
+ },
191
+ "id": "2296"
192
+ },
193
+ {
194
+ "input": {
195
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-003990.bin",
196
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
197
+ "prompt": "users' specifically-defined prompts for different models"
198
+ },
199
+ "output": {
200
+ "answer": ""
201
+ },
202
+ "id": "1041"
203
+ },
204
+ {
205
+ "input": {
206
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-001414.bin",
207
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
208
+ "prompt": "users' specifically-defined prompts for different models"
209
+ },
210
+ "output": {
211
+ "answer": ""
212
+ },
213
+ "id": "1890"
214
+ },
215
+ {
216
+ "input": {
217
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-002034.bin",
218
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
219
+ "prompt": "users' specifically-defined prompts for different models"
220
+ },
221
+ "output": {
222
+ "answer": ""
223
+ },
224
+ "id": "0134"
225
+ },
226
+ {
227
+ "input": {
228
+ "3d_point_file": "comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/3d_points/08-003297.bin",
229
+ "question": "Given the label_names_dict in comprehension/3d_outdoor_semantic_segmentation/semantic_kitti/label_names_dict.json, segment the 3d point cloud",
230
+ "prompt": "users' specifically-defined prompts for different models"
231
+ },
232
+ "output": {
233
+ "answer": ""
234
+ },
235
+ "id": "1561"
236
+ }
237
+ ]
238
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/aircrafts/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/96168894d7331c75acf77e718d93f3e1.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/96168894d7331c75acf77e718d93f3e1.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0287"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/f2d4b8440d4bde5330afbcb38d77d0c3.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/f2d4b8440d4bde5330afbcb38d77d0c3.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0257"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/b785b39d10c33b5de9f07d25f575b2d4.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/b785b39d10c33b5de9f07d25f575b2d4.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0226"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/1887d7e3ec69fe06a5cac89017eae8d1.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/1887d7e3ec69fe06a5cac89017eae8d1.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0258"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/bc3f4d2bf11e1c6382328b5e05417037.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/bc3f4d2bf11e1c6382328b5e05417037.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0117"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/bdb7294dc4742b33bc0909d98a1ff2b4.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/bdb7294dc4742b33bc0909d98a1ff2b4.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0070"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/4a837740b388aa45d8ff6111270336a9.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/4a837740b388aa45d8ff6111270336a9.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0008"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/ad546b049b2246bd609e2d916fa0da27.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/ad546b049b2246bd609e2d916fa0da27.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0107"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/94ce3a5ad2576e73a5cac89017eae8d1.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/94ce3a5ad2576e73a5cac89017eae8d1.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0078"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/958cc8251e6f7d8fa9b2becd26dc2fc1.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/958cc8251e6f7d8fa9b2becd26dc2fc1.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0108"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/d2412f19d33572dc4c3a35cee92bb95b.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/d2412f19d33572dc4c3a35cee92bb95b.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0065"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/48e9c61de4db838d84b83051fa0ae5d2.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/48e9c61de4db838d84b83051fa0ae5d2.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0135"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/b089abdb33c39321afd477f714c68df9.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/b089abdb33c39321afd477f714c68df9.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0133"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/5bc41589eba11a4e15477d594f1fbd99.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/5bc41589eba11a4e15477d594f1fbd99.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0311"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/adb3ea03d7b954255e9e2656aff7dd5b.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/adb3ea03d7b954255e9e2656aff7dd5b.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0162"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/66ae19841350ac2d4ba2821676102936.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/66ae19841350ac2d4ba2821676102936.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0200"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/c2f43dd2c483e0d86b7bd17e458d0dcb.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/c2f43dd2c483e0d86b7bd17e458d0dcb.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0147"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/7bad9d15c0f0d3c03554ccf8c30febe7.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/7bad9d15c0f0d3c03554ccf8c30febe7.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0244"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/67b7039f53759e92f4fb6842b3610149.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/67b7039f53759e92f4fb6842b3610149.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0152"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02691156/ca4ec545363b3b8e8c2814a4ead9cb90.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/aircrafts/3d_points/ca4ec545363b3b8e8c2814a4ead9cb90.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0119"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/furniture/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/1e202e618d6375052945e400e1430726.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/1e202e618d6375052945e400e1430726.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0065"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/198ff59a42a147eb8ac5948d70801389.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/198ff59a42a147eb8ac5948d70801389.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0325"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/aca4c523f999de86febad4f49b26ec52.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/aca4c523f999de86febad4f49b26ec52.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "1171"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/4e928377ae98ed8d99e8bf807e902261.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/4e928377ae98ed8d99e8bf807e902261.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0062"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/9f321f05a7808719ab610b0c94236463.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/9f321f05a7808719ab610b0c94236463.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0774"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/1581d2682187764730bbd4cddd04c77b.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/1581d2682187764730bbd4cddd04c77b.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "1181"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/f28a91c02b1f32c7126e8f891cf24099.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/f28a91c02b1f32c7126e8f891cf24099.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0530"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/d97e2a50640387adf90c06a14471bc6.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/d97e2a50640387adf90c06a14471bc6.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0144"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/4791914b3bcaf57efebad4f49b26ec52.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/4791914b3bcaf57efebad4f49b26ec52.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0123"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/a2baf45f001e118e2c79f7f31759bfa7.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/a2baf45f001e118e2c79f7f31759bfa7.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "1251"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/80ad1f839582d183fbf6f493308acc40.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/80ad1f839582d183fbf6f493308acc40.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0805"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/d151d9f45d8b14536cd661fb5fd95741.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/d151d9f45d8b14536cd661fb5fd95741.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0956"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/b10d84b3a04085b17618b16b281bdf56.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/b10d84b3a04085b17618b16b281bdf56.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0381"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/15647d8895f78f6096a3517f50eeb9f4.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/15647d8895f78f6096a3517f50eeb9f4.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0438"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/bba5ce8555c8fa89ba18ade30e563d37.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/bba5ce8555c8fa89ba18ade30e563d37.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0397"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/1d825a5cd1884610b08fe058d24e7e61.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/1d825a5cd1884610b08fe058d24e7e61.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0432"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/370b45eeeb9b11416f04d49e4de95b59.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/370b45eeeb9b11416f04d49e4de95b59.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0933"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/4a27cb9384782ce33e95c55cb020b7e6.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/4a27cb9384782ce33e95c55cb020b7e6.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "1028"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/de077e0bd6932baef12d7184a2ad3430.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/de077e0bd6932baef12d7184a2ad3430.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0172"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04379243/eee6cf14a56af26ad71f17d6e8238d04.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/furniture/3d_points/eee6cf14a56af26ad71f17d6e8238d04.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0743"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/personal_item/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/d528407fe43b5df193f0194265a9746c.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/d528407fe43b5df193f0194265a9746c.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0016"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/d3972d599036251369da8e7f9a803d12.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/d3972d599036251369da8e7f9a803d12.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0122"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/83b2ecf5caced214e313875ff213ee10.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/83b2ecf5caced214e313875ff213ee10.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0126"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/33da9c54f43be3e17693a84bff425e3.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/33da9c54f43be3e17693a84bff425e3.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0070"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/ebb111e59d33f36f792f3038a6b53389.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/ebb111e59d33f36f792f3038a6b53389.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0083"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/7739bbd9890d0e47965378fe06bd3c02.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/7739bbd9890d0e47965378fe06bd3c02.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0160"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/fba2fc6f7ab8a62a93f0194265a9746c.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/fba2fc6f7ab8a62a93f0194265a9746c.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0146"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/4bcac7ffff02037f624b30f45b82fb10.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/4bcac7ffff02037f624b30f45b82fb10.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0090"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/f411d80eb0dbc9158757b091804bc082.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/f411d80eb0dbc9158757b091804bc082.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0106"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/3527ff33cb9afa1aae4662a5bb3d78e9.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/3527ff33cb9afa1aae4662a5bb3d78e9.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0046"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/4bd2492d56d6b8c537b5646da91e9ed0.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/4bd2492d56d6b8c537b5646da91e9ed0.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0153"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/8dd7df733a5ba17acae98171fea031ef.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/8dd7df733a5ba17acae98171fea031ef.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0048"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/eadc5e7826fa778939257bd6adf678.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/eadc5e7826fa778939257bd6adf678.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0087"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/9e26dcbac33f056c343b0b12983b9982.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/9e26dcbac33f056c343b0b12983b9982.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0030"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/1a96f73d0929bd4793f0194265a9746c.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/1a96f73d0929bd4793f0194265a9746c.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0113"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/3f94fd316d4f2ca1d57700c05b1862d8.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/3f94fd316d4f2ca1d57700c05b1862d8.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0189"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/5beb3384470cfb3e18edda4db8cbb5a0.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/5beb3384470cfb3e18edda4db8cbb5a0.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0206"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/ca9bba09dcae4b487693a84bff425e3.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/ca9bba09dcae4b487693a84bff425e3.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0112"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/4709e55a82a63f64d57700c05b1862d8.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/4709e55a82a63f64d57700c05b1862d8.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0140"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03467517/aa86d20d03b2303593f0194265a9746c.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/personal_item/3d_points/aa86d20d03b2303593f0194265a9746c.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0191"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/tableware/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/2af78c0b040634e5881cd5e2fd8f0f3b.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/2af78c0b040634e5881cd5e2fd8f0f3b.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0081"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/261cc5f20787f50cdbb57594c19a59cf.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/261cc5f20787f50cdbb57594c19a59cf.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0126"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03797390/8f6c86feaa74698d5c91ee20ade72edc.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/8f6c86feaa74698d5c91ee20ade72edc.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0223"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/a1a81e5d0450d463df9c597227d75069.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/a1a81e5d0450d463df9c597227d75069.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0016"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/5a9e0dd068e2436bd7ebac63aa51083.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/5a9e0dd068e2436bd7ebac63aa51083.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0039"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/b3ee8dcfd2d814253e793fd9530521a8.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/b3ee8dcfd2d814253e793fd9530521a8.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0174"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/70bf2aaedbf9499ec889c00efdaf9928.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/70bf2aaedbf9499ec889c00efdaf9928.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0014"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/13a20b16bb59fdc712e1ea5343ce0273.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/13a20b16bb59fdc712e1ea5343ce0273.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0068"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/32e9d8a4b5a141a2615efc34c3b36ef0.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/32e9d8a4b5a141a2615efc34c3b36ef0.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0089"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/57f1439f5161b8d366cff95abd9dc80.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/57f1439f5161b8d366cff95abd9dc80.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0072"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/6ccb43088eda061dbfc838749f053cf9.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/6ccb43088eda061dbfc838749f053cf9.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0137"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/f6eeb5d67c32616648fda83c10428379.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/f6eeb5d67c32616648fda83c10428379.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0041"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/4d08db52c717b74d49c3792a0dc29860.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/4d08db52c717b74d49c3792a0dc29860.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0149"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/b2ed672f1d7c2a34664b3b9b23ddfcbc.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/b2ed672f1d7c2a34664b3b9b23ddfcbc.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0161"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/88d29e1350eda810c066b9622c005c53.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/88d29e1350eda810c066b9622c005c53.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0033"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/6c4c45af95f811e479414e04132a8bef.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/6c4c45af95f811e479414e04132a8bef.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0026"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03797390/d46b98f63a017578ea456f4bbbc96af9.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/d46b98f63a017578ea456f4bbbc96af9.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0216"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/495af808806f1727a753b1b88fff4abb.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/495af808806f1727a753b1b88fff4abb.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0129"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/1f58b59a1b6b06df766fc93a239bada0.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/1f58b59a1b6b06df766fc93a239bada0.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0125"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03636649/be13324c84d2a9d72b151d8b52c53b90.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/tableware/3d_points/be13324c84d2a9d72b151d8b52c53b90.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0020"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/vehicles/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/52a988b361f6cefd685753e73124c6e8.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/52a988b361f6cefd685753e73124c6e8.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0045"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04225987/1510427efa80c92b11c7049de48f3618.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/1510427efa80c92b11c7049de48f3618.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0165"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/3fd1bff496b369f71765540024eb9fef.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/3fd1bff496b369f71765540024eb9fef.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0111"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/73fd19410ce60b83d5dde04c96fd8146.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/73fd19410ce60b83d5dde04c96fd8146.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0075"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/973d75ed9c12836f3d033e6cf82ec72c.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/973d75ed9c12836f3d033e6cf82ec72c.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0128"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/63cc90709837d314a0b2db16370345f0.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/63cc90709837d314a0b2db16370345f0.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0044"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/b0c2225ab347e28f1a48cf85d161a723.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/b0c2225ab347e28f1a48cf85d161a723.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0046"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/e55e06e7a84f49e2b37378f3c85478b4.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/e55e06e7a84f49e2b37378f3c85478b4.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0106"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/4548d86cf7f1c11ad373c34785838ee4.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/4548d86cf7f1c11ad373c34785838ee4.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0124"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/71b00ea32b1810ac373af83f3f2fe606.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/71b00ea32b1810ac373af83f3f2fe606.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0042"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/56cca58a4f37323bd0889537a7d54003.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/56cca58a4f37323bd0889537a7d54003.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0026"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/3a247c8b82bd675c1fbdb352261d023.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/3a247c8b82bd675c1fbdb352261d023.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0118"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/7d59691881b13cc5702956de1b65c053.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/7d59691881b13cc5702956de1b65c053.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0039"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04225987/344e9402d06bd94031145076011658c5.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/344e9402d06bd94031145076011658c5.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0148"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/52f2a2472411fe2e6b418c7d9fedcaa9.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/52f2a2472411fe2e6b418c7d9fedcaa9.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0050"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04225987/48f26ddc704fec2f379c6a1d59ef7283.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/48f26ddc704fec2f379c6a1d59ef7283.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0147"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/86b6dc954e1ca8e948272812609617e2.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/86b6dc954e1ca8e948272812609617e2.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0135"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/04225987/591971ce679ca4b93ad38b993d9e745f.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/591971ce679ca4b93ad38b993d9e745f.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0157"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03790512/3339fcde0648dadd11d830afc8c91af.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/3339fcde0648dadd11d830afc8c91af.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0083"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/02958343/e4886a4d0c6ea960fe21694bd5f519d1.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/vehicles/3d_points/e4886a4d0c6ea960fe21694bd5f519d1.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0056"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/comprehension/3d_part_segmentation/weapons/annotation.json ADDED
@@ -0,0 +1,258 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d_part_segmentation",
3
+ "data_source": "ShapeNetPart",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "3d",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "text"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/d69e028056c9291069654277b747a908.txt",
20
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/d69e028056c9291069654277b747a908.txt",
21
+ "question": "segment the 3D object into parts",
22
+ "prompt": "users' specifically-defined prompts for different models"
23
+ },
24
+ "output": {
25
+ "answer": ""
26
+ },
27
+ "id": "0057"
28
+ },
29
+ {
30
+ "input": {
31
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/906b20dc0a5a5022714112b147c95c8b.txt",
32
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/906b20dc0a5a5022714112b147c95c8b.txt",
33
+ "question": "segment the 3D object into parts",
34
+ "prompt": "users' specifically-defined prompts for different models"
35
+ },
36
+ "output": {
37
+ "answer": ""
38
+ },
39
+ "id": "0000"
40
+ },
41
+ {
42
+ "input": {
43
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/97ed13011e2d85e16029317225a75a9f.txt",
44
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/97ed13011e2d85e16029317225a75a9f.txt",
45
+ "question": "segment the 3D object into parts",
46
+ "prompt": "users' specifically-defined prompts for different models"
47
+ },
48
+ "output": {
49
+ "answer": ""
50
+ },
51
+ "id": "0045"
52
+ },
53
+ {
54
+ "input": {
55
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/8facbe9d9f4da233d15a5887ec2183c9.txt",
56
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/8facbe9d9f4da233d15a5887ec2183c9.txt",
57
+ "question": "segment the 3D object into parts",
58
+ "prompt": "users' specifically-defined prompts for different models"
59
+ },
60
+ "output": {
61
+ "answer": ""
62
+ },
63
+ "id": "0075"
64
+ },
65
+ {
66
+ "input": {
67
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/68c2ea6a1c8c147e30f57e42c77d3894.txt",
68
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/68c2ea6a1c8c147e30f57e42c77d3894.txt",
69
+ "question": "segment the 3D object into parts",
70
+ "prompt": "users' specifically-defined prompts for different models"
71
+ },
72
+ "output": {
73
+ "answer": ""
74
+ },
75
+ "id": "0070"
76
+ },
77
+ {
78
+ "input": {
79
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/1a1a5facf2b73e534e23e9314af9ae57.txt",
80
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/1a1a5facf2b73e534e23e9314af9ae57.txt",
81
+ "question": "segment the 3D object into parts",
82
+ "prompt": "users' specifically-defined prompts for different models"
83
+ },
84
+ "output": {
85
+ "answer": ""
86
+ },
87
+ "id": "0030"
88
+ },
89
+ {
90
+ "input": {
91
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/ba31cc5e739da65b8295dee32711a98b.txt",
92
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/ba31cc5e739da65b8295dee32711a98b.txt",
93
+ "question": "segment the 3D object into parts",
94
+ "prompt": "users' specifically-defined prompts for different models"
95
+ },
96
+ "output": {
97
+ "answer": ""
98
+ },
99
+ "id": "0048"
100
+ },
101
+ {
102
+ "input": {
103
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/5391a808f36b46971638241f3ad71c10.txt",
104
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/5391a808f36b46971638241f3ad71c10.txt",
105
+ "question": "segment the 3D object into parts",
106
+ "prompt": "users' specifically-defined prompts for different models"
107
+ },
108
+ "output": {
109
+ "answer": ""
110
+ },
111
+ "id": "0017"
112
+ },
113
+ {
114
+ "input": {
115
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/df7a65224f295122ed9c5b25fef60d04.txt",
116
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/df7a65224f295122ed9c5b25fef60d04.txt",
117
+ "question": "segment the 3D object into parts",
118
+ "prompt": "users' specifically-defined prompts for different models"
119
+ },
120
+ "output": {
121
+ "answer": ""
122
+ },
123
+ "id": "0074"
124
+ },
125
+ {
126
+ "input": {
127
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/2071c9e5fe60e5886ba5f56f5d8b6131.txt",
128
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/2071c9e5fe60e5886ba5f56f5d8b6131.txt",
129
+ "question": "segment the 3D object into parts",
130
+ "prompt": "users' specifically-defined prompts for different models"
131
+ },
132
+ "output": {
133
+ "answer": ""
134
+ },
135
+ "id": "0050"
136
+ },
137
+ {
138
+ "input": {
139
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/792f252dcb06f042dd56c1edf3f6e336.txt",
140
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/792f252dcb06f042dd56c1edf3f6e336.txt",
141
+ "question": "segment the 3D object into parts",
142
+ "prompt": "users' specifically-defined prompts for different models"
143
+ },
144
+ "output": {
145
+ "answer": ""
146
+ },
147
+ "id": "0071"
148
+ },
149
+ {
150
+ "input": {
151
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/ceeb38ab7929361e76ec14627bf6bbcb.txt",
152
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/ceeb38ab7929361e76ec14627bf6bbcb.txt",
153
+ "question": "segment the 3D object into parts",
154
+ "prompt": "users' specifically-defined prompts for different models"
155
+ },
156
+ "output": {
157
+ "answer": ""
158
+ },
159
+ "id": "0058"
160
+ },
161
+ {
162
+ "input": {
163
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/c19088b4c32c0f1d22b38218e60be05.txt",
164
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/c19088b4c32c0f1d22b38218e60be05.txt",
165
+ "question": "segment the 3D object into parts",
166
+ "prompt": "users' specifically-defined prompts for different models"
167
+ },
168
+ "output": {
169
+ "answer": ""
170
+ },
171
+ "id": "0041"
172
+ },
173
+ {
174
+ "input": {
175
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/2514540f36b2ced12f8c68dc62edc38.txt",
176
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/2514540f36b2ced12f8c68dc62edc38.txt",
177
+ "question": "segment the 3D object into parts",
178
+ "prompt": "users' specifically-defined prompts for different models"
179
+ },
180
+ "output": {
181
+ "answer": ""
182
+ },
183
+ "id": "0052"
184
+ },
185
+ {
186
+ "input": {
187
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/c1ab7029de67351cf97a65c35ea619f0.txt",
188
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/c1ab7029de67351cf97a65c35ea619f0.txt",
189
+ "question": "segment the 3D object into parts",
190
+ "prompt": "users' specifically-defined prompts for different models"
191
+ },
192
+ "output": {
193
+ "answer": ""
194
+ },
195
+ "id": "0044"
196
+ },
197
+ {
198
+ "input": {
199
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/d1c757548ead4a4d8d03ca4865da5b6.txt",
200
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/d1c757548ead4a4d8d03ca4865da5b6.txt",
201
+ "question": "segment the 3D object into parts",
202
+ "prompt": "users' specifically-defined prompts for different models"
203
+ },
204
+ "output": {
205
+ "answer": ""
206
+ },
207
+ "id": "0004"
208
+ },
209
+ {
210
+ "input": {
211
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/fca703c2489237d51b44a9962207f944.txt",
212
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/fca703c2489237d51b44a9962207f944.txt",
213
+ "question": "segment the 3D object into parts",
214
+ "prompt": "users' specifically-defined prompts for different models"
215
+ },
216
+ "output": {
217
+ "answer": ""
218
+ },
219
+ "id": "0076"
220
+ },
221
+ {
222
+ "input": {
223
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/665bf5d30d342d64adee73efb2c043f8.txt",
224
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/665bf5d30d342d64adee73efb2c043f8.txt",
225
+ "question": "segment the 3D object into parts",
226
+ "prompt": "users' specifically-defined prompts for different models"
227
+ },
228
+ "output": {
229
+ "answer": ""
230
+ },
231
+ "id": "0062"
232
+ },
233
+ {
234
+ "input": {
235
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/a105080ce4564145aeb54153795ede63.txt",
236
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/a105080ce4564145aeb54153795ede63.txt",
237
+ "question": "segment the 3D object into parts",
238
+ "prompt": "users' specifically-defined prompts for different models"
239
+ },
240
+ "output": {
241
+ "answer": ""
242
+ },
243
+ "id": "0008"
244
+ },
245
+ {
246
+ "input": {
247
+ "original_3d_point_file": "shapenetcore_partanno_segmentation_benchmark_v0_normal/03624134/d63521a0dfac9c1f342494fa6f09f376.txt",
248
+ "3d_point_file": "comprehension/3d_part_segmentation/weapons/3d_points/d63521a0dfac9c1f342494fa6f09f376.txt",
249
+ "question": "segment the 3D object into parts",
250
+ "prompt": "users' specifically-defined prompts for different models"
251
+ },
252
+ "output": {
253
+ "answer": ""
254
+ },
255
+ "id": "0059"
256
+ }
257
+ ]
258
+ }
Scope-A/S-A-Quick/3D/generation/3d_point_cloud_completion/annotation.json ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "point cloud completion",
3
+ "data_source": "pcc",
4
+ "type": "comprehension",
5
+ "modality": {
6
+ "in": [
7
+ "point cloud",
8
+ "text"
9
+ ],
10
+ "out": [
11
+ "point cloud"
12
+ ]
13
+ },
14
+ "version": "1.0",
15
+ "set_type": "closeset",
16
+ "data": [
17
+ {
18
+ "input": {
19
+ "pc_file": "4171071eb30dcb412dd4967de4160123.ply",
20
+ "prompt": "complete the partial point cloud"
21
+ },
22
+ "output": {
23
+ "pc_file": ""
24
+ },
25
+ "id": "pcc0090"
26
+ },
27
+ {
28
+ "input": {
29
+ "pc_file": "80ef50d3e67bfa8783ca982ecb7c4900.ply",
30
+ "prompt": "complete the partial point cloud"
31
+ },
32
+ "output": {
33
+ "pc_file": ""
34
+ },
35
+ "id": "pcc0032"
36
+ },
37
+ {
38
+ "input": {
39
+ "pc_file": "3e662f9afbe62838f0f8b2d752b1c7a1.ply",
40
+ "prompt": "complete the partial point cloud"
41
+ },
42
+ "output": {
43
+ "pc_file": ""
44
+ },
45
+ "id": "pcc0180"
46
+ },
47
+ {
48
+ "input": {
49
+ "pc_file": "9c0ed8415a0a9e5c49b1e464b71de577.ply",
50
+ "prompt": "complete the partial point cloud"
51
+ },
52
+ "output": {
53
+ "pc_file": ""
54
+ },
55
+ "id": "pcc0245"
56
+ },
57
+ {
58
+ "input": {
59
+ "pc_file": "c7f31b9900a1a7644785ad2feb797e.ply",
60
+ "prompt": "complete the partial point cloud"
61
+ },
62
+ "output": {
63
+ "pc_file": ""
64
+ },
65
+ "id": "pcc0211"
66
+ },
67
+ {
68
+ "input": {
69
+ "pc_file": "3db18530ff6bbb50f130e9cdbbb1cf40.ply",
70
+ "prompt": "complete the partial point cloud"
71
+ },
72
+ "output": {
73
+ "pc_file": ""
74
+ },
75
+ "id": "pcc0170"
76
+ },
77
+ {
78
+ "input": {
79
+ "pc_file": "62d40988172e8c685ff61a3a2a0e2484.ply",
80
+ "prompt": "complete the partial point cloud"
81
+ },
82
+ "output": {
83
+ "pc_file": ""
84
+ },
85
+ "id": "pcc0192"
86
+ },
87
+ {
88
+ "input": {
89
+ "pc_file": "6f87c833355679ef36b0f2a1430e993a.ply",
90
+ "prompt": "complete the partial point cloud"
91
+ },
92
+ "output": {
93
+ "pc_file": ""
94
+ },
95
+ "id": "pcc0128"
96
+ },
97
+ {
98
+ "input": {
99
+ "pc_file": "ffe4383cff6d000a3628187d1bb97b92.ply",
100
+ "prompt": "complete the partial point cloud"
101
+ },
102
+ "output": {
103
+ "pc_file": ""
104
+ },
105
+ "id": "pcc0159"
106
+ },
107
+ {
108
+ "input": {
109
+ "pc_file": "548ab6b6e8b2dc505ff61a3a2a0e2484.ply",
110
+ "prompt": "complete the partial point cloud"
111
+ },
112
+ "output": {
113
+ "pc_file": ""
114
+ },
115
+ "id": "pcc0291"
116
+ }
117
+ ],
118
+ "others": {}
119
+ }
Scope-A/S-A-Quick/3D/generation/rgbd_to_mesh/scannet/annotation_v1.json ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "rgbd_to_mesh",
3
+ "data_source": "scannet",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "rgbd"
8
+ ],
9
+ "out": [
10
+ "mesh"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "set_type": "closeset",
15
+ "data": [
16
+ {
17
+ "input": {
18
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0208_00.sens",
19
+ "question": "generate the mesh for the scene scene0208_00",
20
+ "prompt": "users' specifically-defined prompts for different models"
21
+ },
22
+ "output": {
23
+ "answer": ""
24
+ },
25
+ "id": "0072"
26
+ },
27
+ {
28
+ "input": {
29
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0343_00.sens",
30
+ "question": "generate the mesh for the scene scene0343_00",
31
+ "prompt": "users' specifically-defined prompts for different models"
32
+ },
33
+ "output": {
34
+ "answer": ""
35
+ },
36
+ "id": "0084"
37
+ },
38
+ {
39
+ "input": {
40
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0550_00.sens",
41
+ "question": "generate the mesh for the scene scene0550_00",
42
+ "prompt": "users' specifically-defined prompts for different models"
43
+ },
44
+ "output": {
45
+ "answer": ""
46
+ },
47
+ "id": "0027"
48
+ },
49
+ {
50
+ "input": {
51
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0690_00.sens",
52
+ "question": "generate the mesh for the scene scene0690_00",
53
+ "prompt": "users' specifically-defined prompts for different models"
54
+ },
55
+ "output": {
56
+ "answer": ""
57
+ },
58
+ "id": "0077"
59
+ },
60
+ {
61
+ "input": {
62
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0490_00.sens",
63
+ "question": "generate the mesh for the scene scene0490_00",
64
+ "prompt": "users' specifically-defined prompts for different models"
65
+ },
66
+ "output": {
67
+ "answer": ""
68
+ },
69
+ "id": "0038"
70
+ },
71
+ {
72
+ "input": {
73
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0131_00.sens",
74
+ "question": "generate the mesh for the scene scene0131_00",
75
+ "prompt": "users' specifically-defined prompts for different models"
76
+ },
77
+ "output": {
78
+ "answer": ""
79
+ },
80
+ "id": "0012"
81
+ },
82
+ {
83
+ "input": {
84
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0695_00.sens",
85
+ "question": "generate the mesh for the scene scene0695_00",
86
+ "prompt": "users' specifically-defined prompts for different models"
87
+ },
88
+ "output": {
89
+ "answer": ""
90
+ },
91
+ "id": "0042"
92
+ },
93
+ {
94
+ "input": {
95
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0355_00.sens",
96
+ "question": "generate the mesh for the scene scene0355_00",
97
+ "prompt": "users' specifically-defined prompts for different models"
98
+ },
99
+ "output": {
100
+ "answer": ""
101
+ },
102
+ "id": "0065"
103
+ },
104
+ {
105
+ "input": {
106
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0251_00.sens",
107
+ "question": "generate the mesh for the scene scene0251_00",
108
+ "prompt": "users' specifically-defined prompts for different models"
109
+ },
110
+ "output": {
111
+ "answer": ""
112
+ },
113
+ "id": "0018"
114
+ },
115
+ {
116
+ "input": {
117
+ "rgbd": "generation/rgbd_to_mesh/scannet/rgbd/scene0671_00.sens",
118
+ "question": "generate the mesh for the scene scene0671_00",
119
+ "prompt": "users' specifically-defined prompts for different models"
120
+ },
121
+ "output": {
122
+ "answer": ""
123
+ },
124
+ "id": "0036"
125
+ }
126
+ ]
127
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/culture_and_structure_closeset.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D mesh: ",
18
+ "test_prompt": "a glass windshield",
19
+ "attribute": "texture"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0282"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D mesh: ",
27
+ "test_prompt": "a fluffy bike streamer",
28
+ "attribute": "texture"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0304"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D mesh: ",
36
+ "test_prompt": "a pyramidal artifact",
37
+ "attribute": "shape"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0237"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D mesh: ",
45
+ "test_prompt": "a delivery person sorting packages in a van",
46
+ "attribute": "interaction"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0464"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D mesh: ",
54
+ "test_prompt": "a tall spear",
55
+ "attribute": "shape"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0162"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D mesh: ",
63
+ "test_prompt": "a rustic chalice",
64
+ "attribute": "color"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0014"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D mesh: ",
72
+ "test_prompt": "a white caravan",
73
+ "attribute": "color"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0114"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D mesh: ",
81
+ "test_prompt": "a meteorologist launching a weather balloon",
82
+ "attribute": "interaction"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0490"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D mesh: ",
90
+ "test_prompt": "a sailor holding a compass",
91
+ "attribute": "interaction"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0399"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D mesh: ",
99
+ "test_prompt": "a silver necklace",
100
+ "attribute": "color"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0008"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/living_and_art_closeset.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D mesh: ",
18
+ "test_prompt": "a leather armchair",
19
+ "attribute": "texture"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0254"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D mesh: ",
27
+ "test_prompt": "a pyramidal pastry",
28
+ "attribute": "shape"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0218"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D mesh: ",
36
+ "test_prompt": "a conical abstract lamp",
37
+ "attribute": "shape"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0182"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D mesh: ",
45
+ "test_prompt": "a mug sitting on a coaster",
46
+ "attribute": "interaction"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0386"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D mesh: ",
54
+ "test_prompt": "a floor lamp standing beside a sofa",
55
+ "attribute": "interaction"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0460"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D mesh: ",
63
+ "test_prompt": "a plastic geometric pattern",
64
+ "attribute": "texture"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0287"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D mesh: ",
72
+ "test_prompt": "a metallic table",
73
+ "attribute": "texture"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0252"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D mesh: ",
81
+ "test_prompt": "a plastic decorative model",
82
+ "attribute": "texture"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0297"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D mesh: ",
90
+ "test_prompt": "a fabric lampshade",
91
+ "attribute": "texture"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0354"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D mesh: ",
99
+ "test_prompt": "a crescent headboard",
100
+ "attribute": "shape"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0166"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/nature_and_biology_closeset.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D mesh: ",
18
+ "test_prompt": "a wooden starfish",
19
+ "attribute": "texture"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0344"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D mesh: ",
27
+ "test_prompt": "a pyramidal flower arrangement",
28
+ "attribute": "shape"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0156"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D mesh: ",
36
+ "test_prompt": "a moss-covered garden statue",
37
+ "attribute": "color"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0096"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D mesh: ",
45
+ "test_prompt": "a tree branch cradles a nest.",
46
+ "attribute": "interaction"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0498"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D mesh: ",
54
+ "test_prompt": "a blush pink vase",
55
+ "attribute": "color"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0114"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D mesh: ",
63
+ "test_prompt": "a metallic palm frond",
64
+ "attribute": "texture"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0288"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D mesh: ",
72
+ "test_prompt": "a rectangular log slice",
73
+ "attribute": "shape"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0176"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D mesh: ",
81
+ "test_prompt": "a crescent-shaped fern tip",
82
+ "attribute": "shape"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0180"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D mesh: ",
90
+ "test_prompt": "a teardrop-shaped jellyfish",
91
+ "attribute": "shape"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0204"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D mesh: ",
99
+ "test_prompt": "a glass stingray",
100
+ "attribute": "texture"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0351"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_mesh_gen/science_and_technology_closeset.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D mesh: ",
18
+ "test_prompt": "a drone carrying a sensor device.",
19
+ "attribute": "interaction"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0440"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D mesh: ",
27
+ "test_prompt": "a glossy camera",
28
+ "attribute": "color"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0031"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D mesh: ",
36
+ "test_prompt": "a cubic storage device",
37
+ "attribute": "shape"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0200"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D mesh: ",
45
+ "test_prompt": "a conical testing probe",
46
+ "attribute": "shape"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0175"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D mesh: ",
54
+ "test_prompt": "a leather smartwatch band",
55
+ "attribute": "texture"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0320"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D mesh: ",
63
+ "test_prompt": "a wooden satellite",
64
+ "attribute": "texture"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0254"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D mesh: ",
72
+ "test_prompt": "a spherical gyroscope unit",
73
+ "attribute": "shape"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0189"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D mesh: ",
81
+ "test_prompt": "a fluffy keyboard cleaner",
82
+ "attribute": "texture"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0362"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D mesh: ",
90
+ "test_prompt": "a transparent tablet",
91
+ "attribute": "color"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0005"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D mesh: ",
99
+ "test_prompt": "a futuristic lamp",
100
+ "attribute": "color"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0080"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/culture_and_structure.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
18
+ "test_prompt": "a mason repairing a stone wall of an ancient fortress",
19
+ "attribute": "interaction"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0452"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
27
+ "test_prompt": "a golden dagger",
28
+ "attribute": "color"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0045"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
36
+ "test_prompt": "a painted amphora",
37
+ "attribute": "color"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0062"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
45
+ "test_prompt": "a plastic historical figurine",
46
+ "attribute": "texture"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0332"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
54
+ "test_prompt": "a wooden scepter",
55
+ "attribute": "texture"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0334"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
63
+ "test_prompt": "a researcher writing notes on a clipboard",
64
+ "attribute": "interaction"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0500"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
72
+ "test_prompt": "a glass periscope",
73
+ "attribute": "texture"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0314"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
81
+ "test_prompt": "a scooter rider wearing a bright helmet",
82
+ "attribute": "interaction"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0435"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
90
+ "test_prompt": "a rubber railway sleeper",
91
+ "attribute": "texture"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0291"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
99
+ "test_prompt": "a spherical globe",
100
+ "attribute": "shape"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0242"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/living_and_art.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
18
+ "test_prompt": "a pyramidal sculpture",
19
+ "attribute": "shape"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0137"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
27
+ "test_prompt": "a vintage coffee table",
28
+ "attribute": "color"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0003"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
36
+ "test_prompt": "a plastic mirror frame",
37
+ "attribute": "texture"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0266"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
45
+ "test_prompt": "a rubber coatrack",
46
+ "attribute": "texture"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0274"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
54
+ "test_prompt": "a geometric bar tray",
55
+ "attribute": "color"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0102"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
63
+ "test_prompt": "a pentagonal mirror",
64
+ "attribute": "shape"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0133"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
72
+ "test_prompt": "a rectangular digital print",
73
+ "attribute": "shape"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0196"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
81
+ "test_prompt": "a vintage wine glass",
82
+ "attribute": "color"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0085"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
90
+ "test_prompt": "a fabric floor lamp shade",
91
+ "attribute": "texture"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0360"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
99
+ "test_prompt": "a rubber table lamp base",
100
+ "attribute": "texture"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0358"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/nature_and_biology.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
18
+ "test_prompt": "a minimalist plant stand",
19
+ "attribute": "color"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0063"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
27
+ "test_prompt": "a teardrop leaf tip",
28
+ "attribute": "shape"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0160"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
36
+ "test_prompt": "a leather vine leaf",
37
+ "attribute": "texture"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0298"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
45
+ "test_prompt": "a rubber stem",
46
+ "attribute": "texture"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0276"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
54
+ "test_prompt": "a pentagonal flower cluster",
55
+ "attribute": "shape"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0140"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
63
+ "test_prompt": "a coral starfish ornament",
64
+ "attribute": "color"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0055"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
72
+ "test_prompt": "a bat hangs upside down.",
73
+ "attribute": "interaction"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0437"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
81
+ "test_prompt": "a silver fishbowl",
82
+ "attribute": "color"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0008"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
90
+ "test_prompt": "a metallic fox",
91
+ "attribute": "texture"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0309"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
99
+ "test_prompt": "a hedgehog rolls into a ball.",
100
+ "attribute": "interaction"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0460"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/3D/generation/text_2_3d_pointcloud_gen/science_and_technology.json ADDED
@@ -0,0 +1,107 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "task": "3d generation",
3
+ "data_source": "self-construct",
4
+ "type": "generation",
5
+ "modality": {
6
+ "in": [
7
+ "text"
8
+ ],
9
+ "out": [
10
+ "image"
11
+ ]
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "input": {
17
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
18
+ "test_prompt": "a researcher standing on a balance platform.",
19
+ "attribute": "interaction"
20
+ },
21
+ "output": {},
22
+ "id": "3dgen0460"
23
+ },
24
+ {
25
+ "input": {
26
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
27
+ "test_prompt": "a copper-wired controller",
28
+ "attribute": "color"
29
+ },
30
+ "output": {},
31
+ "id": "3dgen0109"
32
+ },
33
+ {
34
+ "input": {
35
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
36
+ "test_prompt": "a drone taking aerial footage of a city.",
37
+ "attribute": "interaction"
38
+ },
39
+ "output": {},
40
+ "id": "3dgen0481"
41
+ },
42
+ {
43
+ "input": {
44
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
45
+ "test_prompt": "a crescent-shaped led light bar",
46
+ "attribute": "shape"
47
+ },
48
+ "output": {},
49
+ "id": "3dgen0160"
50
+ },
51
+ {
52
+ "input": {
53
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
54
+ "test_prompt": "a cubic computer tower",
55
+ "attribute": "shape"
56
+ },
57
+ "output": {},
58
+ "id": "3dgen0164"
59
+ },
60
+ {
61
+ "input": {
62
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
63
+ "test_prompt": "a pentagonal light sensor",
64
+ "attribute": "shape"
65
+ },
66
+ "output": {},
67
+ "id": "3dgen0230"
68
+ },
69
+ {
70
+ "input": {
71
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
72
+ "test_prompt": "a round ai interface",
73
+ "attribute": "shape"
74
+ },
75
+ "output": {},
76
+ "id": "3dgen0239"
77
+ },
78
+ {
79
+ "input": {
80
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
81
+ "test_prompt": "a plastic audio mixer",
82
+ "attribute": "texture"
83
+ },
84
+ "output": {},
85
+ "id": "3dgen0325"
86
+ },
87
+ {
88
+ "input": {
89
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
90
+ "test_prompt": "a robot dog playing with a ball.",
91
+ "attribute": "interaction"
92
+ },
93
+ "output": {},
94
+ "id": "3dgen0458"
95
+ },
96
+ {
97
+ "input": {
98
+ "system_prompt": "Generate the corresponding 3D point cloud: ",
99
+ "test_prompt": "a rectangular thermal camera",
100
+ "attribute": "shape"
101
+ },
102
+ "output": {},
103
+ "id": "3dgen0228"
104
+ }
105
+ ],
106
+ "set_type": "closeset"
107
+ }
Scope-A/S-A-Quick/audio/comprehension/AccentClassification/annotation.json ADDED
@@ -0,0 +1,256 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "accent classification",
4
+ "data_source": "Speech Accent Archive",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "ac_184",
17
+ "input": {
18
+ "audio_file": "turkish7.mp3",
19
+ "prompt": "what is the accent in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "ac_364",
25
+ "input": {
26
+ "audio_file": "english483.mp3",
27
+ "prompt": "what is the accent in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "ac_281",
33
+ "input": {
34
+ "audio_file": "english141.mp3",
35
+ "prompt": "what is the accent in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "ac_310",
41
+ "input": {
42
+ "audio_file": "english223.mp3",
43
+ "prompt": "what is the accent in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "ac_150",
49
+ "input": {
50
+ "audio_file": "english574.mp3",
51
+ "prompt": "what is the accent in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "ac_185",
57
+ "input": {
58
+ "audio_file": "english478.mp3",
59
+ "prompt": "what is the accent in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "ac_009",
65
+ "input": {
66
+ "audio_file": "polish19.mp3",
67
+ "prompt": "what is the accent in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "ac_323",
73
+ "input": {
74
+ "audio_file": "english399.mp3",
75
+ "prompt": "what is the accent in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "ac_255",
81
+ "input": {
82
+ "audio_file": "english378.mp3",
83
+ "prompt": "what is the accent in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "ac_039",
89
+ "input": {
90
+ "audio_file": "english9.mp3",
91
+ "prompt": "what is the accent in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "ac_036",
97
+ "input": {
98
+ "audio_file": "korean20.mp3",
99
+ "prompt": "what is the accent in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "ac_487",
105
+ "input": {
106
+ "audio_file": "english203.mp3",
107
+ "prompt": "what is the accent in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "ac_491",
113
+ "input": {
114
+ "audio_file": "mandarin64.mp3",
115
+ "prompt": "what is the accent in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "ac_137",
121
+ "input": {
122
+ "audio_file": "english11.mp3",
123
+ "prompt": "what is the accent in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "ac_489",
129
+ "input": {
130
+ "audio_file": "turkish27.mp3",
131
+ "prompt": "what is the accent in the audio?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "ac_068",
137
+ "input": {
138
+ "audio_file": "urdu9.mp3",
139
+ "prompt": "what is the accent in the audio?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "ac_284",
145
+ "input": {
146
+ "audio_file": "mandarin39.mp3",
147
+ "prompt": "what is the accent in the audio?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "ac_381",
153
+ "input": {
154
+ "audio_file": "english292.mp3",
155
+ "prompt": "what is the accent in the audio?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "ac_331",
161
+ "input": {
162
+ "audio_file": "english67.mp3",
163
+ "prompt": "what is the accent in the audio?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "ac_015",
169
+ "input": {
170
+ "audio_file": "english512.mp3",
171
+ "prompt": "what is the accent in the audio?"
172
+ },
173
+ "output": {}
174
+ },
175
+ {
176
+ "id": "ac_075",
177
+ "input": {
178
+ "audio_file": "english265.mp3",
179
+ "prompt": "what is the accent in the audio?"
180
+ },
181
+ "output": {}
182
+ },
183
+ {
184
+ "id": "ac_209",
185
+ "input": {
186
+ "audio_file": "vlaams3.mp3",
187
+ "prompt": "what is the accent in the audio?"
188
+ },
189
+ "output": {}
190
+ },
191
+ {
192
+ "id": "ac_031",
193
+ "input": {
194
+ "audio_file": "english572.mp3",
195
+ "prompt": "what is the accent in the audio?"
196
+ },
197
+ "output": {}
198
+ },
199
+ {
200
+ "id": "ac_416",
201
+ "input": {
202
+ "audio_file": "teochew1.mp3",
203
+ "prompt": "what is the accent in the audio?"
204
+ },
205
+ "output": {}
206
+ },
207
+ {
208
+ "id": "ac_175",
209
+ "input": {
210
+ "audio_file": "english210.mp3",
211
+ "prompt": "what is the accent in the audio?"
212
+ },
213
+ "output": {}
214
+ },
215
+ {
216
+ "id": "ac_077",
217
+ "input": {
218
+ "audio_file": "korean10.mp3",
219
+ "prompt": "what is the accent in the audio?"
220
+ },
221
+ "output": {}
222
+ },
223
+ {
224
+ "id": "ac_090",
225
+ "input": {
226
+ "audio_file": "mandarin3.mp3",
227
+ "prompt": "what is the accent in the audio?"
228
+ },
229
+ "output": {}
230
+ },
231
+ {
232
+ "id": "ac_116",
233
+ "input": {
234
+ "audio_file": "french33.mp3",
235
+ "prompt": "what is the accent in the audio?"
236
+ },
237
+ "output": {}
238
+ },
239
+ {
240
+ "id": "ac_239",
241
+ "input": {
242
+ "audio_file": "korean50.mp3",
243
+ "prompt": "what is the accent in the audio?"
244
+ },
245
+ "output": {}
246
+ },
247
+ {
248
+ "id": "ac_143",
249
+ "input": {
250
+ "audio_file": "english281.mp3",
251
+ "prompt": "what is the accent in the audio?"
252
+ },
253
+ "output": {}
254
+ }
255
+ ]
256
+ }
Scope-A/S-A-Quick/audio/comprehension/AccentSexClassification/annotation.json ADDED
@@ -0,0 +1,256 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "accent sex classification",
4
+ "data_source": "Speech Accent Archive",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "asc_485",
17
+ "input": {
18
+ "audio_file": "japanese14.mp3",
19
+ "prompt": "what is the sex in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "asc_494",
25
+ "input": {
26
+ "audio_file": "afrikaans2.mp3",
27
+ "prompt": "what is the sex in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "asc_331",
33
+ "input": {
34
+ "audio_file": "bulgarian15.mp3",
35
+ "prompt": "what is the sex in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "asc_104",
41
+ "input": {
42
+ "audio_file": "arabic48.mp3",
43
+ "prompt": "what is the sex in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "asc_250",
49
+ "input": {
50
+ "audio_file": "russian24.mp3",
51
+ "prompt": "what is the sex in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "asc_123",
57
+ "input": {
58
+ "audio_file": "norwegian6.mp3",
59
+ "prompt": "what is the sex in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "asc_208",
65
+ "input": {
66
+ "audio_file": "irish1.mp3",
67
+ "prompt": "what is the sex in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "asc_192",
73
+ "input": {
74
+ "audio_file": "spanish80.mp3",
75
+ "prompt": "what is the sex in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "asc_076",
81
+ "input": {
82
+ "audio_file": "korean12.mp3",
83
+ "prompt": "what is the sex in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "asc_097",
89
+ "input": {
90
+ "audio_file": "dari3.mp3",
91
+ "prompt": "what is the sex in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "asc_314",
97
+ "input": {
98
+ "audio_file": "hindi18.mp3",
99
+ "prompt": "what is the sex in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "asc_086",
105
+ "input": {
106
+ "audio_file": "english26.mp3",
107
+ "prompt": "what is the sex in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "asc_030",
113
+ "input": {
114
+ "audio_file": "english205.mp3",
115
+ "prompt": "what is the sex in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "asc_491",
121
+ "input": {
122
+ "audio_file": "english434.mp3",
123
+ "prompt": "what is the sex in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "asc_239",
129
+ "input": {
130
+ "audio_file": "portuguese9.mp3",
131
+ "prompt": "what is the sex in the audio?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "asc_158",
137
+ "input": {
138
+ "audio_file": "english199.mp3",
139
+ "prompt": "what is the sex in the audio?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "asc_478",
145
+ "input": {
146
+ "audio_file": "spanish25.mp3",
147
+ "prompt": "what is the sex in the audio?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "asc_431",
153
+ "input": {
154
+ "audio_file": "farsi11.mp3",
155
+ "prompt": "what is the sex in the audio?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "asc_271",
161
+ "input": {
162
+ "audio_file": "english550.mp3",
163
+ "prompt": "what is the sex in the audio?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "asc_341",
169
+ "input": {
170
+ "audio_file": "english382.mp3",
171
+ "prompt": "what is the sex in the audio?"
172
+ },
173
+ "output": {}
174
+ },
175
+ {
176
+ "id": "asc_462",
177
+ "input": {
178
+ "audio_file": "english42.mp3",
179
+ "prompt": "what is the sex in the audio?"
180
+ },
181
+ "output": {}
182
+ },
183
+ {
184
+ "id": "asc_384",
185
+ "input": {
186
+ "audio_file": "macedonian15.mp3",
187
+ "prompt": "what is the sex in the audio?"
188
+ },
189
+ "output": {}
190
+ },
191
+ {
192
+ "id": "asc_265",
193
+ "input": {
194
+ "audio_file": "korean43.mp3",
195
+ "prompt": "what is the sex in the audio?"
196
+ },
197
+ "output": {}
198
+ },
199
+ {
200
+ "id": "asc_347",
201
+ "input": {
202
+ "audio_file": "italian31.mp3",
203
+ "prompt": "what is the sex in the audio?"
204
+ },
205
+ "output": {}
206
+ },
207
+ {
208
+ "id": "asc_031",
209
+ "input": {
210
+ "audio_file": "arabic64.mp3",
211
+ "prompt": "what is the sex in the audio?"
212
+ },
213
+ "output": {}
214
+ },
215
+ {
216
+ "id": "asc_461",
217
+ "input": {
218
+ "audio_file": "arabic43.mp3",
219
+ "prompt": "what is the sex in the audio?"
220
+ },
221
+ "output": {}
222
+ },
223
+ {
224
+ "id": "asc_418",
225
+ "input": {
226
+ "audio_file": "kikongo1.mp3",
227
+ "prompt": "what is the sex in the audio?"
228
+ },
229
+ "output": {}
230
+ },
231
+ {
232
+ "id": "asc_057",
233
+ "input": {
234
+ "audio_file": "english207.mp3",
235
+ "prompt": "what is the sex in the audio?"
236
+ },
237
+ "output": {}
238
+ },
239
+ {
240
+ "id": "asc_036",
241
+ "input": {
242
+ "audio_file": "russian27.mp3",
243
+ "prompt": "what is the sex in the audio?"
244
+ },
245
+ "output": {}
246
+ },
247
+ {
248
+ "id": "asc_312",
249
+ "input": {
250
+ "audio_file": "kikuyu4.mp3",
251
+ "prompt": "what is the sex in the audio?"
252
+ },
253
+ "output": {}
254
+ }
255
+ ]
256
+ }
Scope-A/S-A-Quick/audio/comprehension/AcousticSceneRecognition/annotation.json ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "Acoustic Scene Classification",
4
+ "data_source": "TUT Acoustic Scenes 2017",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data_description": "The TUT Acoustic Scenes 2017 dataset is a dataset for acoustic scene classification. The dataset contains audio recordings of 15 different acoustic scenes. The scenes are divided into three categories: indoor, outdoor, and transportation.",
15
+ "data": [
16
+ {
17
+ "id": "asc_00177",
18
+ "input": {
19
+ "audio_file": "a139_110_120.wav",
20
+ "prompt": "what is the scene in the audio?"
21
+ },
22
+ "output": {}
23
+ },
24
+ {
25
+ "id": "asc_00247",
26
+ "input": {
27
+ "audio_file": "a008_170_180.wav",
28
+ "prompt": "what is the scene in the audio?"
29
+ },
30
+ "output": {}
31
+ },
32
+ {
33
+ "id": "asc_00439",
34
+ "input": {
35
+ "audio_file": "a009_100_110.wav",
36
+ "prompt": "what is the scene in the audio?"
37
+ },
38
+ "output": {}
39
+ },
40
+ {
41
+ "id": "asc_00287",
42
+ "input": {
43
+ "audio_file": "a109_130_140.wav",
44
+ "prompt": "what is the scene in the audio?"
45
+ },
46
+ "output": {}
47
+ },
48
+ {
49
+ "id": "asc_00382",
50
+ "input": {
51
+ "audio_file": "b061_80_90.wav",
52
+ "prompt": "what is the scene in the audio?"
53
+ },
54
+ "output": {}
55
+ },
56
+ {
57
+ "id": "asc_00097",
58
+ "input": {
59
+ "audio_file": "a151_260_270.wav",
60
+ "prompt": "what is the scene in the audio?"
61
+ },
62
+ "output": {}
63
+ },
64
+ {
65
+ "id": "asc_00026",
66
+ "input": {
67
+ "audio_file": "a071_10_20.wav",
68
+ "prompt": "what is the scene in the audio?"
69
+ },
70
+ "output": {}
71
+ },
72
+ {
73
+ "id": "asc_00119",
74
+ "input": {
75
+ "audio_file": "b049_30_40.wav",
76
+ "prompt": "what is the scene in the audio?"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "asc_00381",
82
+ "input": {
83
+ "audio_file": "b015_10_20.wav",
84
+ "prompt": "what is the scene in the audio?"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "asc_00078",
90
+ "input": {
91
+ "audio_file": "a098_190_200.wav",
92
+ "prompt": "what is the scene in the audio?"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "asc_00209",
98
+ "input": {
99
+ "audio_file": "b070_160_170.wav",
100
+ "prompt": "what is the scene in the audio?"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "asc_00045",
106
+ "input": {
107
+ "audio_file": "b110_200_210.wav",
108
+ "prompt": "what is the scene in the audio?"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "asc_00126",
114
+ "input": {
115
+ "audio_file": "b041_170_180.wav",
116
+ "prompt": "what is the scene in the audio?"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "asc_00427",
122
+ "input": {
123
+ "audio_file": "a057_100_110.wav",
124
+ "prompt": "what is the scene in the audio?"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "asc_00304",
130
+ "input": {
131
+ "audio_file": "b050_140_150.wav",
132
+ "prompt": "what is the scene in the audio?"
133
+ },
134
+ "output": {}
135
+ }
136
+ ]
137
+ }
Scope-A/S-A-Quick/audio/comprehension/AnimalSoundDetection/annotation.json ADDED
@@ -0,0 +1,177 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "animal sound detection",
4
+ "data_source": "Animal Sound Detection challenge",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "description": "The dataset contains audio recordings of 10 different animals, including cat, dog, bird, cow, lion, sheep, frog, chicken, donkey, monkey. The task is to detect what an animal sound is in the audio.",
15
+ "data": [
16
+ {
17
+ "id": "asd_297",
18
+ "input": {
19
+ "audio_file": "Kedi-Part2/cat_158.wav",
20
+ "prompt": "what is the animal sound in the audio?"
21
+ },
22
+ "output": {}
23
+ },
24
+ {
25
+ "id": "asd_234",
26
+ "input": {
27
+ "audio_file": "Inek/inek_17.wav",
28
+ "prompt": "what is the animal sound in the audio?"
29
+ },
30
+ "output": {}
31
+ },
32
+ {
33
+ "id": "asd_451",
34
+ "input": {
35
+ "audio_file": "Kus-Part2/Kus_197.wav",
36
+ "prompt": "what is the animal sound in the audio?"
37
+ },
38
+ "output": {}
39
+ },
40
+ {
41
+ "id": "asd_261",
42
+ "input": {
43
+ "audio_file": "Kus-Part1/Kus_40.wav",
44
+ "prompt": "what is the animal sound in the audio?"
45
+ },
46
+ "output": {}
47
+ },
48
+ {
49
+ "id": "asd_114",
50
+ "input": {
51
+ "audio_file": "Kedi-Part2/cat_123.wav",
52
+ "prompt": "what is the animal sound in the audio?"
53
+ },
54
+ "output": {}
55
+ },
56
+ {
57
+ "id": "asd_442",
58
+ "input": {
59
+ "audio_file": "Tavuk/tavuk_11.wav",
60
+ "prompt": "what is the animal sound in the audio?"
61
+ },
62
+ "output": {}
63
+ },
64
+ {
65
+ "id": "asd_097",
66
+ "input": {
67
+ "audio_file": "Kopek-Part1/dog_83.wav",
68
+ "prompt": "what is the animal sound in the audio?"
69
+ },
70
+ "output": {}
71
+ },
72
+ {
73
+ "id": "asd_394",
74
+ "input": {
75
+ "audio_file": "Kus-Part1/Kus_29.wav",
76
+ "prompt": "what is the animal sound in the audio?"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "asd_477",
82
+ "input": {
83
+ "audio_file": "Inek/inek_52.wav",
84
+ "prompt": "what is the animal sound in the audio?"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "asd_431",
90
+ "input": {
91
+ "audio_file": "Kopek-Part2/dog_148.wav",
92
+ "prompt": "what is the animal sound in the audio?"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "asd_244",
98
+ "input": {
99
+ "audio_file": "Kopek-Part2/dog_191.wav",
100
+ "prompt": "what is the animal sound in the audio?"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "asd_108",
106
+ "input": {
107
+ "audio_file": "Kopek-Part1/dog_85.wav",
108
+ "prompt": "what is the animal sound in the audio?"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "asd_016",
114
+ "input": {
115
+ "audio_file": "Aslan/aslan_43.wav",
116
+ "prompt": "what is the animal sound in the audio?"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "asd_015",
122
+ "input": {
123
+ "audio_file": "Inek/inek_2.wav",
124
+ "prompt": "what is the animal sound in the audio?"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "asd_320",
130
+ "input": {
131
+ "audio_file": "Kus-Part1/Kus_78.wav",
132
+ "prompt": "what is the animal sound in the audio?"
133
+ },
134
+ "output": {}
135
+ },
136
+ {
137
+ "id": "asd_417",
138
+ "input": {
139
+ "audio_file": "Kedi-Part1/cat_51.wav",
140
+ "prompt": "what is the animal sound in the audio?"
141
+ },
142
+ "output": {}
143
+ },
144
+ {
145
+ "id": "asd_437",
146
+ "input": {
147
+ "audio_file": "Kopek-Part1/dog_55.wav",
148
+ "prompt": "what is the animal sound in the audio?"
149
+ },
150
+ "output": {}
151
+ },
152
+ {
153
+ "id": "asd_249",
154
+ "input": {
155
+ "audio_file": "Aslan/aslan_8.wav",
156
+ "prompt": "what is the animal sound in the audio?"
157
+ },
158
+ "output": {}
159
+ },
160
+ {
161
+ "id": "asd_322",
162
+ "input": {
163
+ "audio_file": "Maymun/maymun_15.wav",
164
+ "prompt": "what is the animal sound in the audio?"
165
+ },
166
+ "output": {}
167
+ },
168
+ {
169
+ "id": "asd_147",
170
+ "input": {
171
+ "audio_file": "Kurbaga/kurbaga_32.wav",
172
+ "prompt": "what is the animal sound in the audio?"
173
+ },
174
+ "output": {}
175
+ }
176
+ ]
177
+ }
Scope-A/S-A-Quick/audio/comprehension/AudioQuestionAnswering/annotation.json ADDED
@@ -0,0 +1,256 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "audio question answering",
4
+ "data_source": "Clotho-AQA",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "aqa_312",
17
+ "input": {
18
+ "audio_file": "chrrrckkkcc.wav",
19
+ "prompt": "Can impact noises be heard ?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "aqa_382",
25
+ "input": {
26
+ "audio_file": "cowshed.wav",
27
+ "prompt": "Is there more than one cow?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "aqa_182",
33
+ "input": {
34
+ "audio_file": "Cardiff Bay fireworks.wav",
35
+ "prompt": "Is the sky lighting up with colors?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "aqa_141",
41
+ "input": {
42
+ "audio_file": "Jet Engine 1.wav",
43
+ "prompt": "is the saw turned on?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "aqa_393",
49
+ "input": {
50
+ "audio_file": "spring rain in the woods.wav",
51
+ "prompt": "Does the water dripping sound get louder?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "aqa_195",
57
+ "input": {
58
+ "audio_file": "Bird Ambience.wav",
59
+ "prompt": "Is this outdoors ?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "aqa_139",
65
+ "input": {
66
+ "audio_file": "Cityscape construction site 2 100304.wav",
67
+ "prompt": "Can a car be heard in the background?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "aqa_322",
73
+ "input": {
74
+ "audio_file": "water dripping 2.wav",
75
+ "prompt": "Is water dripping?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "aqa_007",
81
+ "input": {
82
+ "audio_file": "By ther blacksmith-002.wav",
83
+ "prompt": "Is something metallic being hit?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "aqa_297",
89
+ "input": {
90
+ "audio_file": "Atmosphere on road in London.wav",
91
+ "prompt": "Are all of the sounds heard coming from something close?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "aqa_392",
97
+ "input": {
98
+ "audio_file": "maryam sounds 5.wav",
99
+ "prompt": "Is something jangling?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "aqa_305",
105
+ "input": {
106
+ "audio_file": "Cars_bridge.wav",
107
+ "prompt": "Are there any vehicles around?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "aqa_438",
113
+ "input": {
114
+ "audio_file": "broken comms2.wav",
115
+ "prompt": "Is the voice broken and choppy?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "aqa_439",
121
+ "input": {
122
+ "audio_file": "Air raid siren_rising.wav",
123
+ "prompt": "Are the birds singing?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "aqa_262",
129
+ "input": {
130
+ "audio_file": "SCC CLAPTER 20101210.wav",
131
+ "prompt": "Is there only a single person in the audience?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "aqa_209",
137
+ "input": {
138
+ "audio_file": "Construction Sounds.wav",
139
+ "prompt": "Is this loud?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "aqa_175",
145
+ "input": {
146
+ "audio_file": "20090412.bell.strikes.12.wav",
147
+ "prompt": "Can dogs be heard barking?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "aqa_286",
153
+ "input": {
154
+ "audio_file": "Short Hailstorm.wav",
155
+ "prompt": "Does the rain let up first, and then pick up steam?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "aqa_272",
161
+ "input": {
162
+ "audio_file": "air bubbles on the surface of the water.wav",
163
+ "prompt": "Can bubbles be heard?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "aqa_394",
169
+ "input": {
170
+ "audio_file": "TIKTOK_1.wav",
171
+ "prompt": "Is something keeping time?"
172
+ },
173
+ "output": {}
174
+ },
175
+ {
176
+ "id": "aqa_271",
177
+ "input": {
178
+ "audio_file": "Lincoln Nebraska Tornado 5 9 2016.wav",
179
+ "prompt": "Is it loudest at the beginning?"
180
+ },
181
+ "output": {}
182
+ },
183
+ {
184
+ "id": "aqa_060",
185
+ "input": {
186
+ "audio_file": "moving glass pieces.wav",
187
+ "prompt": "Are there repetitive clinking sounds?"
188
+ },
189
+ "output": {}
190
+ },
191
+ {
192
+ "id": "aqa_355",
193
+ "input": {
194
+ "audio_file": "20080918.boots.door.wav",
195
+ "prompt": "Is this person outside?"
196
+ },
197
+ "output": {}
198
+ },
199
+ {
200
+ "id": "aqa_472",
201
+ "input": {
202
+ "audio_file": "Westland Petrels land in their colony at dawn.wav",
203
+ "prompt": "Can a voice be heard?"
204
+ },
205
+ "output": {}
206
+ },
207
+ {
208
+ "id": "aqa_450",
209
+ "input": {
210
+ "audio_file": "Sound_FX_Ambient_Street_cars+passing_by.wav",
211
+ "prompt": "Can a child be heard?"
212
+ },
213
+ "output": {}
214
+ },
215
+ {
216
+ "id": "aqa_479",
217
+ "input": {
218
+ "audio_file": "tua-mirandela_train_arrival_march2007.wav",
219
+ "prompt": "Is the person talking?"
220
+ },
221
+ "output": {}
222
+ },
223
+ {
224
+ "id": "aqa_255",
225
+ "input": {
226
+ "audio_file": "md1trk33-34.wav",
227
+ "prompt": "Is there a quick fix to remedy this sound?"
228
+ },
229
+ "output": {}
230
+ },
231
+ {
232
+ "id": "aqa_111",
233
+ "input": {
234
+ "audio_file": "Wind-up Crank.wav",
235
+ "prompt": "Is the squealing noise unnatural?"
236
+ },
237
+ "output": {}
238
+ },
239
+ {
240
+ "id": "aqa_396",
241
+ "input": {
242
+ "audio_file": "Thunder Rain Cars Driving By.wav",
243
+ "prompt": "Is the ground dry?"
244
+ },
245
+ "output": {}
246
+ },
247
+ {
248
+ "id": "aqa_147",
249
+ "input": {
250
+ "audio_file": "market.wav",
251
+ "prompt": "Are there people having conversations?"
252
+ },
253
+ "output": {}
254
+ }
255
+ ]
256
+ }
Scope-A/S-A-Quick/audio/comprehension/BirdSoundDetection/annotation.json ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "bird sound detection",
4
+ "data_source": "Bird Audio Detection challenge",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "label": {
15
+ "0": "no bird sound",
16
+ "1": "bird sound"
17
+ },
18
+ "data": [
19
+ {
20
+ "id": "bsd_305",
21
+ "input": {
22
+ "audio_file": "138c8d9d-2058-4f0b-b643.wav",
23
+ "prompt": "is there a bird sound in the audio?"
24
+ },
25
+ "output": {}
26
+ },
27
+ {
28
+ "id": "bsd_046",
29
+ "input": {
30
+ "audio_file": "d4c97395-df78-403d-9c24.wav",
31
+ "prompt": "is there a bird sound in the audio?"
32
+ },
33
+ "output": {}
34
+ },
35
+ {
36
+ "id": "bsd_003",
37
+ "input": {
38
+ "audio_file": "837f0bcd-ae19-46d9-b523.wav",
39
+ "prompt": "is there a bird sound in the audio?"
40
+ },
41
+ "output": {}
42
+ },
43
+ {
44
+ "id": "bsd_262",
45
+ "input": {
46
+ "audio_file": "958f8316-e179-412d-a617.wav",
47
+ "prompt": "is there a bird sound in the audio?"
48
+ },
49
+ "output": {}
50
+ },
51
+ {
52
+ "id": "bsd_490",
53
+ "input": {
54
+ "audio_file": "367a9493-a136-4927-b511.wav",
55
+ "prompt": "is there a bird sound in the audio?"
56
+ },
57
+ "output": {}
58
+ },
59
+ {
60
+ "id": "bsd_141",
61
+ "input": {
62
+ "audio_file": "49285ba6-3ade-4473-8a19.wav",
63
+ "prompt": "is there a bird sound in the audio?"
64
+ },
65
+ "output": {}
66
+ },
67
+ {
68
+ "id": "bsd_338",
69
+ "input": {
70
+ "audio_file": "08e08bd2-448c-4cb3-801b.wav",
71
+ "prompt": "is there a bird sound in the audio?"
72
+ },
73
+ "output": {}
74
+ },
75
+ {
76
+ "id": "bsd_390",
77
+ "input": {
78
+ "audio_file": "fdcfa53a-bf73-4bf7-a868.wav",
79
+ "prompt": "is there a bird sound in the audio?"
80
+ },
81
+ "output": {}
82
+ },
83
+ {
84
+ "id": "bsd_314",
85
+ "input": {
86
+ "audio_file": "5122a4c3-d987-474b-8494.wav",
87
+ "prompt": "is there a bird sound in the audio?"
88
+ },
89
+ "output": {}
90
+ },
91
+ {
92
+ "id": "bsd_255",
93
+ "input": {
94
+ "audio_file": "b9df707d-da24-4137-837f.wav",
95
+ "prompt": "is there a bird sound in the audio?"
96
+ },
97
+ "output": {}
98
+ },
99
+ {
100
+ "id": "bsd_289",
101
+ "input": {
102
+ "audio_file": "8dc781a4-25d7-4414-b23d.wav",
103
+ "prompt": "is there a bird sound in the audio?"
104
+ },
105
+ "output": {}
106
+ },
107
+ {
108
+ "id": "bsd_447",
109
+ "input": {
110
+ "audio_file": "6668e69a-b69c-49fc-9f0f.wav",
111
+ "prompt": "is there a bird sound in the audio?"
112
+ },
113
+ "output": {}
114
+ },
115
+ {
116
+ "id": "bsd_195",
117
+ "input": {
118
+ "audio_file": "8054df7d-7171-401b-a96e.wav",
119
+ "prompt": "is there a bird sound in the audio?"
120
+ },
121
+ "output": {}
122
+ },
123
+ {
124
+ "id": "bsd_157",
125
+ "input": {
126
+ "audio_file": "041dca15-e775-4c09-88d5.wav",
127
+ "prompt": "is there a bird sound in the audio?"
128
+ },
129
+ "output": {}
130
+ },
131
+ {
132
+ "id": "bsd_410",
133
+ "input": {
134
+ "audio_file": "51beaa05-962a-4a9b-ad58.wav",
135
+ "prompt": "is there a bird sound in the audio?"
136
+ },
137
+ "output": {}
138
+ },
139
+ {
140
+ "id": "bsd_265",
141
+ "input": {
142
+ "audio_file": "363c1377-3522-4d17-9d1b.wav",
143
+ "prompt": "is there a bird sound in the audio?"
144
+ },
145
+ "output": {}
146
+ },
147
+ {
148
+ "id": "bsd_184",
149
+ "input": {
150
+ "audio_file": "7e74d733-0937-439d-be55.wav",
151
+ "prompt": "is there a bird sound in the audio?"
152
+ },
153
+ "output": {}
154
+ },
155
+ {
156
+ "id": "bsd_453",
157
+ "input": {
158
+ "audio_file": "c90ab208-019f-4b93-9703.wav",
159
+ "prompt": "is there a bird sound in the audio?"
160
+ },
161
+ "output": {}
162
+ },
163
+ {
164
+ "id": "bsd_347",
165
+ "input": {
166
+ "audio_file": "2f6f00f8-b811-4736-a9e9.wav",
167
+ "prompt": "is there a bird sound in the audio?"
168
+ },
169
+ "output": {}
170
+ },
171
+ {
172
+ "id": "bsd_331",
173
+ "input": {
174
+ "audio_file": "375109fb-129c-42eb-b913.wav",
175
+ "prompt": "is there a bird sound in the audio?"
176
+ },
177
+ "output": {}
178
+ }
179
+ ]
180
+ }
Scope-A/S-A-Quick/audio/comprehension/EnvironmentSoundRecognition/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "environment sound classification",
4
+ "data_source": "ESC-50",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "esc_461",
17
+ "input": {
18
+ "audio_file": "2-87795-A-24.wav",
19
+ "prompt": "what is the sound in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "esc_287",
25
+ "input": {
26
+ "audio_file": "2-152964-A-31.wav",
27
+ "prompt": "what is the sound in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "esc_423",
33
+ "input": {
34
+ "audio_file": "4-158653-A-32.wav",
35
+ "prompt": "what is the sound in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "esc_006",
41
+ "input": {
42
+ "audio_file": "4-197454-A-28.wav",
43
+ "prompt": "what is the sound in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "esc_079",
49
+ "input": {
50
+ "audio_file": "3-144128-A-23.wav",
51
+ "prompt": "what is the sound in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "esc_489",
57
+ "input": {
58
+ "audio_file": "5-238021-A-35.wav",
59
+ "prompt": "what is the sound in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "esc_462",
65
+ "input": {
66
+ "audio_file": "5-235671-A-38.wav",
67
+ "prompt": "what is the sound in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "esc_002",
73
+ "input": {
74
+ "audio_file": "2-122066-A-45.wav",
75
+ "prompt": "what is the sound in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "esc_016",
81
+ "input": {
82
+ "audio_file": "1-211527-A-20.wav",
83
+ "prompt": "what is the sound in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "esc_126",
89
+ "input": {
90
+ "audio_file": "5-192191-A-19.wav",
91
+ "prompt": "what is the sound in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "esc_354",
97
+ "input": {
98
+ "audio_file": "3-100018-A-18.wav",
99
+ "prompt": "what is the sound in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "esc_414",
105
+ "input": {
106
+ "audio_file": "2-153388-A-31.wav",
107
+ "prompt": "what is the sound in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "esc_078",
113
+ "input": {
114
+ "audio_file": "2-144031-A-34.wav",
115
+ "prompt": "what is the sound in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "esc_338",
121
+ "input": {
122
+ "audio_file": "1-9887-B-49.wav",
123
+ "prompt": "what is the sound in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "esc_481",
129
+ "input": {
130
+ "audio_file": "2-188822-C-40.wav",
131
+ "prompt": "what is the sound in the audio?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/IntentClassification/annotation.json ADDED
@@ -0,0 +1,233 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "intent classification",
4
+ "data_source": "FluentSpeechCommands",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "intent_label": {
15
+ "action": {
16
+ "change language": 0,
17
+ "0": "change language",
18
+ "activate": 1,
19
+ "1": "activate",
20
+ "deactivate": 2,
21
+ "2": "deactivate",
22
+ "increase": 3,
23
+ "3": "increase",
24
+ "decrease": 4,
25
+ "4": "decrease",
26
+ "bring": 5,
27
+ "5": "bring"
28
+ },
29
+ "object": {
30
+ "none": 0,
31
+ "0": "none",
32
+ "music": 1,
33
+ "1": "music",
34
+ "lights": 2,
35
+ "2": "lights",
36
+ "volume": 3,
37
+ "3": "volume",
38
+ "heat": 4,
39
+ "4": "heat",
40
+ "lamp": 5,
41
+ "5": "lamp",
42
+ "newspaper": 6,
43
+ "6": "newspaper",
44
+ "juice": 7,
45
+ "7": "juice",
46
+ "socks": 8,
47
+ "8": "socks",
48
+ "Chinese": 9,
49
+ "9": "Chinese",
50
+ "Korean": 10,
51
+ "10": "Korean",
52
+ "English": 11,
53
+ "11": "English",
54
+ "German": 12,
55
+ "12": "German",
56
+ "shoes": 13,
57
+ "13": "shoes"
58
+ },
59
+ "location": {
60
+ "none": 0,
61
+ "0": "none",
62
+ "kitchen": 1,
63
+ "1": "kitchen",
64
+ "bedroom": 2,
65
+ "2": "bedroom",
66
+ "washroom": 3,
67
+ "3": "washroom"
68
+ }
69
+ },
70
+ "output_text_description": "The output text is the intent of the audio. The intent is composed of three parts: action, object, and location. The action is the action that the speaker is doing in the audio. The object is the object that the speaker is interacting with in the audio. The location is the location where the speaker is in the audio.",
71
+ "data": [
72
+ {
73
+ "id": "ic_00896",
74
+ "input": {
75
+ "audio_file": "7B4XmNppyrCK977p/c0b9c390-45ce-11e9-9992-dfba019cb646.wav",
76
+ "prompt": "what is the intent in the audio?"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "ic_02583",
82
+ "input": {
83
+ "audio_file": "ppymZZDb2Bf4NQnE/b44bf7d0-44f3-11e9-a1ea-79ca03012c0e.wav",
84
+ "prompt": "what is the intent in the audio?"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "ic_03488",
90
+ "input": {
91
+ "audio_file": "V4ZbwLm9G5irobWn/b1de4b00-4526-11e9-a843-8db76f4b5e29.wav",
92
+ "prompt": "what is the intent in the audio?"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "ic_01917",
98
+ "input": {
99
+ "audio_file": "NgQEvO2x7Vh3xy2xz/5af3ec00-45d0-11e9-9992-dfba019cb646.wav",
100
+ "prompt": "what is the intent in the audio?"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "ic_00260",
106
+ "input": {
107
+ "audio_file": "4BrX8aDqK2cLZRYl/66cedad0-4530-11e9-b1e4-e5985dca719e.wav",
108
+ "prompt": "what is the intent in the audio?"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "ic_02900",
114
+ "input": {
115
+ "audio_file": "V4ejqNL4xbUKkYrV/de55a0d0-4598-11e9-bf4b-216bfa333ac2.wav",
116
+ "prompt": "what is the intent in the audio?"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "ic_00577",
122
+ "input": {
123
+ "audio_file": "7B4XmNppyrCK977p/0ba6cff0-45cb-11e9-9992-dfba019cb646.wav",
124
+ "prompt": "what is the intent in the audio?"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "ic_00488",
130
+ "input": {
131
+ "audio_file": "7B4XmNppyrCK977p/abca5310-45c8-11e9-8ec0-7bf21d1cfe30.wav",
132
+ "prompt": "what is the intent in the audio?"
133
+ },
134
+ "output": {}
135
+ },
136
+ {
137
+ "id": "ic_01823",
138
+ "input": {
139
+ "audio_file": "NgQEvO2x7Vh3xy2xz/ff7148d0-45cc-11e9-9992-dfba019cb646.wav",
140
+ "prompt": "what is the intent in the audio?"
141
+ },
142
+ "output": {}
143
+ },
144
+ {
145
+ "id": "ic_01264",
146
+ "input": {
147
+ "audio_file": "k5bqyxx2lzIbrlg9/12b65b60-4528-11e9-a843-8db76f4b5e29.wav",
148
+ "prompt": "what is the intent in the audio?"
149
+ },
150
+ "output": {}
151
+ },
152
+ {
153
+ "id": "ic_00697",
154
+ "input": {
155
+ "audio_file": "7B4XmNppyrCK977p/931395d0-45cc-11e9-9992-dfba019cb646.wav",
156
+ "prompt": "what is the intent in the audio?"
157
+ },
158
+ "output": {}
159
+ },
160
+ {
161
+ "id": "ic_02607",
162
+ "input": {
163
+ "audio_file": "ppymZZDb2Bf4NQnE/162b8290-44f4-11e9-a1ea-79ca03012c0e.wav",
164
+ "prompt": "what is the intent in the audio?"
165
+ },
166
+ "output": {}
167
+ },
168
+ {
169
+ "id": "ic_03341",
170
+ "input": {
171
+ "audio_file": "V4ZbwLm9G5irobWn/9ea7b140-4524-11e9-a843-8db76f4b5e29.wav",
172
+ "prompt": "what is the intent in the audio?"
173
+ },
174
+ "output": {}
175
+ },
176
+ {
177
+ "id": "ic_00137",
178
+ "input": {
179
+ "audio_file": "4BrX8aDqK2cLZRYl/a322f4a0-452e-11e9-b1e4-e5985dca719e.wav",
180
+ "prompt": "what is the intent in the audio?"
181
+ },
182
+ "output": {}
183
+ },
184
+ {
185
+ "id": "ic_03562",
186
+ "input": {
187
+ "audio_file": "V4ZbwLm9G5irobWn/6d9265b0-4528-11e9-a843-8db76f4b5e29.wav",
188
+ "prompt": "what is the intent in the audio?"
189
+ },
190
+ "output": {}
191
+ },
192
+ {
193
+ "id": "ic_00712",
194
+ "input": {
195
+ "audio_file": "7B4XmNppyrCK977p/bb9686c0-45cc-11e9-9992-dfba019cb646.wav",
196
+ "prompt": "what is the intent in the audio?"
197
+ },
198
+ "output": {}
199
+ },
200
+ {
201
+ "id": "ic_01663",
202
+ "input": {
203
+ "audio_file": "NgQEvO2x7Vh3xy2xz/04847de0-45c0-11e9-8ec0-7bf21d1cfe30.wav",
204
+ "prompt": "what is the intent in the audio?"
205
+ },
206
+ "output": {}
207
+ },
208
+ {
209
+ "id": "ic_03321",
210
+ "input": {
211
+ "audio_file": "V4ZbwLm9G5irobWn/5a57bf30-4524-11e9-a843-8db76f4b5e29.wav",
212
+ "prompt": "what is the intent in the audio?"
213
+ },
214
+ "output": {}
215
+ },
216
+ {
217
+ "id": "ic_00880",
218
+ "input": {
219
+ "audio_file": "7B4XmNppyrCK977p/8ca47b40-45ce-11e9-9992-dfba019cb646.wav",
220
+ "prompt": "what is the intent in the audio?"
221
+ },
222
+ "output": {}
223
+ },
224
+ {
225
+ "id": "ic_00844",
226
+ "input": {
227
+ "audio_file": "7B4XmNppyrCK977p/2a313c00-45ce-11e9-9992-dfba019cb646.wav",
228
+ "prompt": "what is the intent in the audio?"
229
+ },
230
+ "output": {}
231
+ }
232
+ ]
233
+ }
Scope-A/S-A-Quick/audio/comprehension/LongAudioCaptioning/annotation.json ADDED
@@ -0,0 +1,416 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "audio captioning",
4
+ "data_source": "Clotho",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "cc_289",
17
+ "input": {
18
+ "audio_file": "06 - 333 con tren hotel saliendo de la estacion de Zamora hacia Galicia.wav",
19
+ "prompt": "Listen to the clip and write a description."
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "cc_253",
25
+ "input": {
26
+ "audio_file": "Howler monkey and other monkey or bird.wav",
27
+ "prompt": "Listen to the audio and give a description."
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "cc_222",
33
+ "input": {
34
+ "audio_file": "rain.wav",
35
+ "prompt": "Listen to the track and summarize it."
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "cc_175",
41
+ "input": {
42
+ "audio_file": "Heavy Wind.wav",
43
+ "prompt": "Observe the audio and give an account of it."
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "cc_249",
49
+ "input": {
50
+ "audio_file": "birmingham-aston-canal-extractor-fan-background.wav",
51
+ "prompt": "Observe the audio and give an account of it."
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "cc_439",
57
+ "input": {
58
+ "audio_file": "enoesque-Thunder and Rain 1.wav",
59
+ "prompt": "Hear the recording and summarize it briefly."
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "cc_384",
65
+ "input": {
66
+ "audio_file": "Sepang Beach 04.wav",
67
+ "prompt": "Listen to the clip and write a description."
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "cc_454",
73
+ "input": {
74
+ "audio_file": "heavy traffic with ambulance siren.wav",
75
+ "prompt": "Check the audio and provide an overview."
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "cc_414",
81
+ "input": {
82
+ "audio_file": "heating_far away.wav",
83
+ "prompt": "Review the audio and offer a description."
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "cc_467",
89
+ "input": {
90
+ "audio_file": "Queen Street Mill, loom running then stops.wav",
91
+ "prompt": "Check the audio and provide an overview."
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "cc_408",
97
+ "input": {
98
+ "audio_file": "Storm coming.wav",
99
+ "prompt": "Hear the recording and summarize it briefly."
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "cc_493",
105
+ "input": {
106
+ "audio_file": "Outside02.wav",
107
+ "prompt": "Analyze the recording and describe it."
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "cc_119",
113
+ "input": {
114
+ "audio_file": "Water dripping.wav",
115
+ "prompt": "Analyze the recording and describe it."
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "cc_405",
121
+ "input": {
122
+ "audio_file": "Train passing by and horning in Romania (Bacau). Close recording.wav",
123
+ "prompt": "Observe the audio and give an account of it."
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "cc_479",
129
+ "input": {
130
+ "audio_file": "driveaway.wav",
131
+ "prompt": "Analyze the recording and describe it."
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "cc_148",
137
+ "input": {
138
+ "audio_file": "01 barreau bunker original.wav",
139
+ "prompt": "Analyze the recording and describe it."
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "cc_180",
145
+ "input": {
146
+ "audio_file": "Plaza_de_la_Revolucion_risa.wav",
147
+ "prompt": "Listen to the track and summarize it."
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "cc_416",
153
+ "input": {
154
+ "audio_file": "Weinglaser.wav",
155
+ "prompt": "Analyze the recording and describe it."
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "cc_281",
161
+ "input": {
162
+ "audio_file": "Appartment_Ambient_AC_TV_Fans.wav",
163
+ "prompt": "Analyze the recording and describe it."
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "cc_066",
169
+ "input": {
170
+ "audio_file": "junk_box001.wav",
171
+ "prompt": "Hear the recording and summarize it briefly."
172
+ },
173
+ "output": {}
174
+ },
175
+ {
176
+ "id": "cc_353",
177
+ "input": {
178
+ "audio_file": "Bounce-MagnetAndNail.wav",
179
+ "prompt": "Listen to the audio and give a description."
180
+ },
181
+ "output": {}
182
+ },
183
+ {
184
+ "id": "cc_103",
185
+ "input": {
186
+ "audio_file": "Cruiseship - passenger library.wav",
187
+ "prompt": "Listen to the audio and give a description."
188
+ },
189
+ "output": {}
190
+ },
191
+ {
192
+ "id": "cc_057",
193
+ "input": {
194
+ "audio_file": "Air raid siren_rising.wav",
195
+ "prompt": "Listen to the track and summarize it."
196
+ },
197
+ "output": {}
198
+ },
199
+ {
200
+ "id": "cc_464",
201
+ "input": {
202
+ "audio_file": "Forbidden Purr02.wav",
203
+ "prompt": "Hear the recording and summarize it briefly."
204
+ },
205
+ "output": {}
206
+ },
207
+ {
208
+ "id": "cc_280",
209
+ "input": {
210
+ "audio_file": "Railroad Crossing Japan.wav",
211
+ "prompt": "Review the audio and offer a description."
212
+ },
213
+ "output": {}
214
+ },
215
+ {
216
+ "id": "cc_033",
217
+ "input": {
218
+ "audio_file": "Cityscape 05 090617.wav",
219
+ "prompt": "Listen to the audio and give a description."
220
+ },
221
+ "output": {}
222
+ },
223
+ {
224
+ "id": "cc_172",
225
+ "input": {
226
+ "audio_file": "Ambience birds.wav",
227
+ "prompt": "Hear the audio and write a brief description."
228
+ },
229
+ "output": {}
230
+ },
231
+ {
232
+ "id": "cc_158",
233
+ "input": {
234
+ "audio_file": "2013-03-28 rain in the rainforest.wav",
235
+ "prompt": "Listen to the track and summarize it."
236
+ },
237
+ "output": {}
238
+ },
239
+ {
240
+ "id": "cc_391",
241
+ "input": {
242
+ "audio_file": "RBH_Household_shower 03.wav",
243
+ "prompt": "Listen to the clip and write a description."
244
+ },
245
+ "output": {}
246
+ },
247
+ {
248
+ "id": "cc_356",
249
+ "input": {
250
+ "audio_file": "Gazpoile_long.wav",
251
+ "prompt": "Hear the sound and explain what you hear."
252
+ },
253
+ "output": {}
254
+ },
255
+ {
256
+ "id": "cc_147",
257
+ "input": {
258
+ "audio_file": "Urban Snow Melt.wav",
259
+ "prompt": "Analyze the recording and describe it."
260
+ },
261
+ "output": {}
262
+ },
263
+ {
264
+ "id": "cc_352",
265
+ "input": {
266
+ "audio_file": "Plane Over Traffic.wav",
267
+ "prompt": "Listen to the track and summarize it."
268
+ },
269
+ "output": {}
270
+ },
271
+ {
272
+ "id": "cc_335",
273
+ "input": {
274
+ "audio_file": "Chopping Celery.wav",
275
+ "prompt": "Review the audio and offer a description."
276
+ },
277
+ "output": {}
278
+ },
279
+ {
280
+ "id": "cc_450",
281
+ "input": {
282
+ "audio_file": "Regent's conversation.wav",
283
+ "prompt": "Review the audio and offer a description."
284
+ },
285
+ "output": {}
286
+ },
287
+ {
288
+ "id": "cc_150",
289
+ "input": {
290
+ "audio_file": "Fountain Trompenburg 090928.wav",
291
+ "prompt": "Listen to the audio and give a description."
292
+ },
293
+ "output": {}
294
+ },
295
+ {
296
+ "id": "cc_124",
297
+ "input": {
298
+ "audio_file": "water_vipS.wav",
299
+ "prompt": "Listen to the audio and give a description."
300
+ },
301
+ "output": {}
302
+ },
303
+ {
304
+ "id": "cc_395",
305
+ "input": {
306
+ "audio_file": "Walking in Kitchen.wav",
307
+ "prompt": "Listen to the clip and write a description."
308
+ },
309
+ "output": {}
310
+ },
311
+ {
312
+ "id": "cc_011",
313
+ "input": {
314
+ "audio_file": "Street_Car.wav",
315
+ "prompt": "Analyze the recording and describe it."
316
+ },
317
+ "output": {}
318
+ },
319
+ {
320
+ "id": "cc_045",
321
+ "input": {
322
+ "audio_file": "pouring water (dif speeds).wav",
323
+ "prompt": "Listen to the audio and give a description."
324
+ },
325
+ "output": {}
326
+ },
327
+ {
328
+ "id": "cc_481",
329
+ "input": {
330
+ "audio_file": "forest_ambiance_chepachet_spring_night_2.wav",
331
+ "prompt": "Hear the recording and summarize it briefly."
332
+ },
333
+ "output": {}
334
+ },
335
+ {
336
+ "id": "cc_056",
337
+ "input": {
338
+ "audio_file": "Streatham Railway Station and on a Train.wav",
339
+ "prompt": "Hear the recording and summarize it briefly."
340
+ },
341
+ "output": {}
342
+ },
343
+ {
344
+ "id": "cc_038",
345
+ "input": {
346
+ "audio_file": "BangingOilTank.wav",
347
+ "prompt": "Check the audio and provide an overview."
348
+ },
349
+ "output": {}
350
+ },
351
+ {
352
+ "id": "cc_332",
353
+ "input": {
354
+ "audio_file": "Water_Lapping_River.wav",
355
+ "prompt": "Listen to the audio and give a description."
356
+ },
357
+ "output": {}
358
+ },
359
+ {
360
+ "id": "cc_109",
361
+ "input": {
362
+ "audio_file": "big-machine-fan.wav",
363
+ "prompt": "Listen to the track and summarize it."
364
+ },
365
+ "output": {}
366
+ },
367
+ {
368
+ "id": "cc_069",
369
+ "input": {
370
+ "audio_file": "Mockingbird singing @ Alfama.wav",
371
+ "prompt": "Hear the sound and explain what you hear."
372
+ },
373
+ "output": {}
374
+ },
375
+ {
376
+ "id": "cc_471",
377
+ "input": {
378
+ "audio_file": "creaky boxcars.wav",
379
+ "prompt": "Listen to the track and summarize it."
380
+ },
381
+ "output": {}
382
+ },
383
+ {
384
+ "id": "cc_455",
385
+ "input": {
386
+ "audio_file": "Ambiance, Carnival.wav",
387
+ "prompt": "Hear the audio and write a brief description."
388
+ },
389
+ "output": {}
390
+ },
391
+ {
392
+ "id": "cc_278",
393
+ "input": {
394
+ "audio_file": "CarEntireInternal01.wav",
395
+ "prompt": "Observe the audio and give an account of it."
396
+ },
397
+ "output": {}
398
+ },
399
+ {
400
+ "id": "cc_426",
401
+ "input": {
402
+ "audio_file": "Broom_Bear_Street_Sweeper_Roadwork.wav",
403
+ "prompt": "Listen to the audio and give a description."
404
+ },
405
+ "output": {}
406
+ },
407
+ {
408
+ "id": "cc_231",
409
+ "input": {
410
+ "audio_file": "RYTH_door.wav",
411
+ "prompt": "Hear the recording and summarize it briefly."
412
+ },
413
+ "output": {}
414
+ }
415
+ ]
416
+ }
Scope-A/S-A-Quick/audio/comprehension/MusicGenreClassification/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "music genre classification",
4
+ "data_source": "GTZAN",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "mgc_119",
17
+ "input": {
18
+ "audio_file": "pop\\pop.00067.wav",
19
+ "prompt": "what is the genre of the music in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "mgc_017",
25
+ "input": {
26
+ "audio_file": "blues\\blues.00046.wav",
27
+ "prompt": "what is the genre of the music in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "mgc_291",
33
+ "input": {
34
+ "audio_file": "rock\\rock.00012.wav",
35
+ "prompt": "what is the genre of the music in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "mgc_404",
41
+ "input": {
42
+ "audio_file": "disco\\disco.00036.wav",
43
+ "prompt": "what is the genre of the music in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "mgc_409",
49
+ "input": {
50
+ "audio_file": "reggae\\reggae.00081.wav",
51
+ "prompt": "what is the genre of the music in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "mgc_483",
57
+ "input": {
58
+ "audio_file": "disco\\disco.00033.wav",
59
+ "prompt": "what is the genre of the music in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "mgc_365",
65
+ "input": {
66
+ "audio_file": "pop\\pop.00046.wav",
67
+ "prompt": "what is the genre of the music in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "mgc_469",
73
+ "input": {
74
+ "audio_file": "country\\country.00016.wav",
75
+ "prompt": "what is the genre of the music in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "mgc_114",
81
+ "input": {
82
+ "audio_file": "disco\\disco.00022.wav",
83
+ "prompt": "what is the genre of the music in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "mgc_304",
89
+ "input": {
90
+ "audio_file": "pop\\pop.00050.wav",
91
+ "prompt": "what is the genre of the music in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "mgc_003",
97
+ "input": {
98
+ "audio_file": "country\\country.00042.wav",
99
+ "prompt": "what is the genre of the music in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "mgc_112",
105
+ "input": {
106
+ "audio_file": "classical\\classical.00088.wav",
107
+ "prompt": "what is the genre of the music in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "mgc_477",
113
+ "input": {
114
+ "audio_file": "blues\\blues.00077.wav",
115
+ "prompt": "what is the genre of the music in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "mgc_172",
121
+ "input": {
122
+ "audio_file": "country\\country.00096.wav",
123
+ "prompt": "what is the genre of the music in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "mgc_423",
129
+ "input": {
130
+ "audio_file": "blues\\blues.00053.wav",
131
+ "prompt": "what is the genre of the music in the audio?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/MusicInstrumentClassification/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "music instrument analysis",
4
+ "data_source": "Nsynth instrucment",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio"
9
+ ],
10
+ "out": "text"
11
+ },
12
+ "version": "1.0",
13
+ "data_description": "The Nsynth Pitch dataset is a dataset for music instrument analysis. The dataset contains audio recordings of musical notes. There are 11 instruments, including bass, brass, flute, guitar, , keyboard, mallet, organ, reed, string, synth_lead, vocal.",
14
+ "data": [
15
+ {
16
+ "id": "mia_451",
17
+ "input": {
18
+ "audio_file": "bass_synthetic_134-071-050.wav",
19
+ "prompt": "what is the instrument used in the music?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "mia_373",
25
+ "input": {
26
+ "audio_file": "bass_synthetic_034-054-100.wav",
27
+ "prompt": "what is the instrument used in the music?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "mia_076",
33
+ "input": {
34
+ "audio_file": "bass_synthetic_034-100-025.wav",
35
+ "prompt": "what is the instrument used in the music?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "mia_179",
41
+ "input": {
42
+ "audio_file": "keyboard_acoustic_004-034-075.wav",
43
+ "prompt": "what is the instrument used in the music?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "mia_163",
49
+ "input": {
50
+ "audio_file": "keyboard_electronic_078-030-127.wav",
51
+ "prompt": "what is the instrument used in the music?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "mia_432",
57
+ "input": {
58
+ "audio_file": "guitar_acoustic_010-024-075.wav",
59
+ "prompt": "what is the instrument used in the music?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "mia_336",
65
+ "input": {
66
+ "audio_file": "bass_electronic_027-031-050.wav",
67
+ "prompt": "what is the instrument used in the music?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "mia_437",
73
+ "input": {
74
+ "audio_file": "string_acoustic_071-034-127.wav",
75
+ "prompt": "what is the instrument used in the music?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "mia_324",
81
+ "input": {
82
+ "audio_file": "bass_synthetic_009-012-025.wav",
83
+ "prompt": "what is the instrument used in the music?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "mia_358",
89
+ "input": {
90
+ "audio_file": "keyboard_electronic_003-095-100.wav",
91
+ "prompt": "what is the instrument used in the music?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "mia_223",
97
+ "input": {
98
+ "audio_file": "keyboard_electronic_003-073-075.wav",
99
+ "prompt": "what is the instrument used in the music?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "mia_369",
105
+ "input": {
106
+ "audio_file": "keyboard_electronic_098-057-127.wav",
107
+ "prompt": "what is the instrument used in the music?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "mia_362",
113
+ "input": {
114
+ "audio_file": "bass_synthetic_009-086-075.wav",
115
+ "prompt": "what is the instrument used in the music?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "mia_418",
121
+ "input": {
122
+ "audio_file": "vocal_acoustic_000-050-025.wav",
123
+ "prompt": "what is the instrument used in the music?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "mia_317",
129
+ "input": {
130
+ "audio_file": "guitar_acoustic_010-097-050.wav",
131
+ "prompt": "what is the instrument used in the music?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/MusicInstrumentSourceAnalysis/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "music instrument source analysis",
4
+ "data_source": "Nsynth",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio"
9
+ ],
10
+ "out": "text"
11
+ },
12
+ "version": "1.0",
13
+ "data_description": "The Nsynth dataset is a dataset for the sonic source analysis for this instrument . The method of sound production for the note's instrument, including acoustic, electronic, synthetic",
14
+ "data": [
15
+ {
16
+ "id": "isa_323",
17
+ "input": {
18
+ "audio_file": "keyboard_electronic_098-102-127.wav",
19
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "isa_069",
25
+ "input": {
26
+ "audio_file": "bass_electronic_027-032-127.wav",
27
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "isa_132",
33
+ "input": {
34
+ "audio_file": "guitar_electronic_028-082-100.wav",
35
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "isa_155",
41
+ "input": {
42
+ "audio_file": "reed_acoustic_023-035-050.wav",
43
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "isa_023",
49
+ "input": {
50
+ "audio_file": "keyboard_electronic_001-107-100.wav",
51
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "isa_495",
57
+ "input": {
58
+ "audio_file": "keyboard_electronic_078-059-100.wav",
59
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "isa_316",
65
+ "input": {
66
+ "audio_file": "keyboard_electronic_003-045-075.wav",
67
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "isa_110",
73
+ "input": {
74
+ "audio_file": "organ_electronic_104-071-100.wav",
75
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "isa_090",
81
+ "input": {
82
+ "audio_file": "keyboard_electronic_001-040-075.wav",
83
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "isa_059",
89
+ "input": {
90
+ "audio_file": "keyboard_electronic_003-095-100.wav",
91
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "isa_390",
97
+ "input": {
98
+ "audio_file": "bass_synthetic_033-073-050.wav",
99
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "isa_209",
105
+ "input": {
106
+ "audio_file": "mallet_acoustic_047-093-127.wav",
107
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "isa_194",
113
+ "input": {
114
+ "audio_file": "string_acoustic_014-036-100.wav",
115
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "isa_146",
121
+ "input": {
122
+ "audio_file": "vocal_synthetic_003-104-075.wav",
123
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "isa_037",
129
+ "input": {
130
+ "audio_file": "string_acoustic_057-061-100.wav",
131
+ "prompt": "what is the method of sound production for the note's instrument used in the music?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/MusicPitchAnalysis/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "music pitch analysis",
4
+ "data_source": "Nsynth Pitch",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio"
9
+ ],
10
+ "out": "text"
11
+ },
12
+ "version": "1.0",
13
+ "data_description": "The Nsynth Pitch dataset is a dataset for music pitch analysis. The dataset contains audio recordings of musical notes. The 0-based MIDI pitch in the range [0, 127].",
14
+ "data": [
15
+ {
16
+ "id": "mpa_477",
17
+ "input": {
18
+ "audio_file": "bass_electronic_018-057-100.wav",
19
+ "prompt": "what is the pitch of the music in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "mpa_400",
25
+ "input": {
26
+ "audio_file": "brass_acoustic_015-056-025.wav",
27
+ "prompt": "what is the pitch of the music in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "mpa_355",
33
+ "input": {
34
+ "audio_file": "keyboard_electronic_001-047-127.wav",
35
+ "prompt": "what is the pitch of the music in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "mpa_436",
41
+ "input": {
42
+ "audio_file": "bass_synthetic_068-042-025.wav",
43
+ "prompt": "what is the pitch of the music in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "mpa_376",
49
+ "input": {
50
+ "audio_file": "bass_synthetic_098-067-127.wav",
51
+ "prompt": "what is the pitch of the music in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "mpa_218",
57
+ "input": {
58
+ "audio_file": "organ_electronic_028-032-075.wav",
59
+ "prompt": "what is the pitch of the music in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "mpa_003",
65
+ "input": {
66
+ "audio_file": "string_acoustic_014-055-025.wav",
67
+ "prompt": "what is the pitch of the music in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "mpa_042",
73
+ "input": {
74
+ "audio_file": "mallet_acoustic_047-104-075.wav",
75
+ "prompt": "what is the pitch of the music in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "mpa_192",
81
+ "input": {
82
+ "audio_file": "guitar_acoustic_021-101-100.wav",
83
+ "prompt": "what is the pitch of the music in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "mpa_031",
89
+ "input": {
90
+ "audio_file": "organ_electronic_001-064-025.wav",
91
+ "prompt": "what is the pitch of the music in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "mpa_126",
97
+ "input": {
98
+ "audio_file": "mallet_acoustic_062-085-100.wav",
99
+ "prompt": "what is the pitch of the music in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "mpa_357",
105
+ "input": {
106
+ "audio_file": "brass_acoustic_016-066-127.wav",
107
+ "prompt": "what is the pitch of the music in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "mpa_433",
113
+ "input": {
114
+ "audio_file": "bass_electronic_025-048-025.wav",
115
+ "prompt": "what is the pitch of the music in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "mpa_183",
121
+ "input": {
122
+ "audio_file": "guitar_acoustic_030-040-127.wav",
123
+ "prompt": "what is the pitch of the music in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "mpa_072",
129
+ "input": {
130
+ "audio_file": "keyboard_electronic_002-039-127.wav",
131
+ "prompt": "what is the pitch of the music in the audio?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/NoteQualitiesAnalysis/annotation.json ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "music note quality analysis",
4
+ "data_source": "Nsynth Note Qualities",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio"
9
+ ],
10
+ "out": "text"
11
+ },
12
+ "version": "1.0",
13
+ "data_description": "The Nsynth Pitch dataset is a dataset for music note quality analysis. We provide quality annotations for the 10 different note qualities described below. None of the tags are mutually exclusive by definition except for \u201cbright\u201d and \u201cdark\u201d. However, it is possible for a note to be neither \u201cbright\u201d nor \u201cdark\u201d.",
14
+ "data": [
15
+ {
16
+ "id": "nqa_262",
17
+ "input": {
18
+ "audio_file": "string_acoustic_080-033-127.wav",
19
+ "prompt": "what is the instrument used in the music?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "nqa_294",
25
+ "input": {
26
+ "audio_file": "organ_electronic_057-030-075.wav",
27
+ "prompt": "what is the instrument used in the music?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "nqa_449",
33
+ "input": {
34
+ "audio_file": "organ_electronic_057-050-075.wav",
35
+ "prompt": "what is the instrument used in the music?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "nqa_024",
41
+ "input": {
42
+ "audio_file": "keyboard_synthetic_000-040-025.wav",
43
+ "prompt": "what is the instrument used in the music?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "nqa_097",
49
+ "input": {
50
+ "audio_file": "organ_electronic_028-034-075.wav",
51
+ "prompt": "what is the instrument used in the music?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "nqa_140",
57
+ "input": {
58
+ "audio_file": "bass_synthetic_068-100-050.wav",
59
+ "prompt": "what is the instrument used in the music?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "nqa_388",
65
+ "input": {
66
+ "audio_file": "bass_synthetic_009-022-127.wav",
67
+ "prompt": "what is the instrument used in the music?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "nqa_318",
73
+ "input": {
74
+ "audio_file": "organ_electronic_007-023-025.wav",
75
+ "prompt": "what is the instrument used in the music?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "nqa_432",
81
+ "input": {
82
+ "audio_file": "keyboard_electronic_001-048-075.wav",
83
+ "prompt": "what is the instrument used in the music?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "nqa_146",
89
+ "input": {
90
+ "audio_file": "guitar_acoustic_015-023-050.wav",
91
+ "prompt": "what is the instrument used in the music?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "nqa_104",
97
+ "input": {
98
+ "audio_file": "keyboard_synthetic_000-091-100.wav",
99
+ "prompt": "what is the instrument used in the music?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "nqa_333",
105
+ "input": {
106
+ "audio_file": "organ_electronic_028-032-075.wav",
107
+ "prompt": "what is the instrument used in the music?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "nqa_426",
113
+ "input": {
114
+ "audio_file": "bass_synthetic_134-064-025.wav",
115
+ "prompt": "what is the instrument used in the music?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "nqa_297",
121
+ "input": {
122
+ "audio_file": "keyboard_electronic_002-076-127.wav",
123
+ "prompt": "what is the instrument used in the music?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "nqa_444",
129
+ "input": {
130
+ "audio_file": "bass_synthetic_135-037-100.wav",
131
+ "prompt": "what is the instrument used in the music?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "nqa_373",
137
+ "input": {
138
+ "audio_file": "string_acoustic_080-033-075.wav",
139
+ "prompt": "what is the instrument used in the music?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "nqa_420",
145
+ "input": {
146
+ "audio_file": "flute_synthetic_000-048-127.wav",
147
+ "prompt": "what is the instrument used in the music?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "nqa_196",
153
+ "input": {
154
+ "audio_file": "guitar_acoustic_015-104-050.wav",
155
+ "prompt": "what is the instrument used in the music?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "nqa_431",
161
+ "input": {
162
+ "audio_file": "bass_synthetic_134-032-050.wav",
163
+ "prompt": "what is the instrument used in the music?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "nqa_433",
169
+ "input": {
170
+ "audio_file": "bass_synthetic_098-027-127.wav",
171
+ "prompt": "what is the instrument used in the music?"
172
+ },
173
+ "output": {}
174
+ }
175
+ ]
176
+ }
Scope-A/S-A-Quick/audio/comprehension/OpenAudioQuestionAnswering/annotation.json ADDED
@@ -0,0 +1,256 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "Open-ended Question Answering",
4
+ "data_source": "OpenAQA (LTU) ",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "opqa_218",
17
+ "input": {
18
+ "audio_file": "RDEi-N2tCQg.flac",
19
+ "prompt": "Why does the sound of mechanisms occur for a longer duration?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "opqa_324",
25
+ "input": {
26
+ "audio_file": "247225.wav",
27
+ "prompt": "What other musical instruments might be playing in this audio clip?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "opqa_357",
33
+ "input": {
34
+ "audio_file": "382886.wav",
35
+ "prompt": "What kind of scenario could this audio clip come from and why?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "opqa_449",
41
+ "input": {
42
+ "audio_file": "331985.flac",
43
+ "prompt": "Is the knocking sound patterned or random?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "opqa_368",
49
+ "input": {
50
+ "audio_file": "551514.flac",
51
+ "prompt": "What type of microphone was used to record the audio clip?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "opqa_323",
57
+ "input": {
58
+ "audio_file": "82507.flac",
59
+ "prompt": "What musical genre is typically associated with breakbeats?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "opqa_304",
65
+ "input": {
66
+ "audio_file": "HBawAl3vH3U.flac",
67
+ "prompt": "What can we infer from the presence of a toilet flush sound in the audio clip?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "opqa_202",
73
+ "input": {
74
+ "audio_file": "402820.flac",
75
+ "prompt": "What could be inferred from the reverb at the end of the white noise section?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "opqa_193",
81
+ "input": {
82
+ "audio_file": "zCbOiYolXrQ.flac",
83
+ "prompt": "What is the acoustic feature of the wind sound?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "opqa_108",
89
+ "input": {
90
+ "audio_file": "54924.wav",
91
+ "prompt": "What kind of setting or scenario could potentially give rise to rich human voice combined with loud and contagious laughter?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "opqa_491",
97
+ "input": {
98
+ "audio_file": "Crowds_in_Street_in_Seville.wav",
99
+ "prompt": "What might be some of the emotions or sentiments that the people in the audio clip are feeling?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "opqa_352",
105
+ "input": {
106
+ "audio_file": "nkFMcjk04aI.flac",
107
+ "prompt": "What type of laughter can be heard in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "opqa_334",
113
+ "input": {
114
+ "audio_file": "24312.wav",
115
+ "prompt": "What emotion could be conveyed through the full and rich voice in this audio clip?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "opqa_024",
121
+ "input": {
122
+ "audio_file": "24312.wav",
123
+ "prompt": "Based on the acoustic features, what could be the possible gender of the person who is whispering?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "opqa_010",
129
+ "input": {
130
+ "audio_file": "pBPg9RYg2lU.flac",
131
+ "prompt": "What emotions or feelings does the sound of the mechanisms convey?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "opqa_431",
137
+ "input": {
138
+ "audio_file": "Submarine_Diesel_Engine_002.wav",
139
+ "prompt": "What is happening in the audio clip?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "opqa_490",
145
+ "input": {
146
+ "audio_file": "551514.flac",
147
+ "prompt": "What is the friend's reaction to reading the name in the game?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "opqa_000",
153
+ "input": {
154
+ "audio_file": "_jPH-NvvTno.flac",
155
+ "prompt": "What can be inferred based on the combination of sound events in the clip?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "opqa_454",
161
+ "input": {
162
+ "audio_file": "5EFYHBn7660.flac",
163
+ "prompt": "What is the temporal relationship between the rain and boat sound events?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "opqa_321",
169
+ "input": {
170
+ "audio_file": "16061.flac",
171
+ "prompt": "What is the mood or atmosphere conveyed by the synthesizer house loop in the audio clip?"
172
+ },
173
+ "output": {}
174
+ },
175
+ {
176
+ "id": "opqa_210",
177
+ "input": {
178
+ "audio_file": "k4yNgOC-cD8.flac",
179
+ "prompt": "What is the acoustic feature of the male speech?"
180
+ },
181
+ "output": {}
182
+ },
183
+ {
184
+ "id": "opqa_289",
185
+ "input": {
186
+ "audio_file": "aah1FLl5EjU.flac",
187
+ "prompt": "What is the timbre of the sound of music?"
188
+ },
189
+ "output": {}
190
+ },
191
+ {
192
+ "id": "opqa_101",
193
+ "input": {
194
+ "audio_file": "-4BWpdVUMWc.flac",
195
+ "prompt": "At what time does the sound of the tick occur in the audio clip?"
196
+ },
197
+ "output": {}
198
+ },
199
+ {
200
+ "id": "opqa_367",
201
+ "input": {
202
+ "audio_file": "s8BzZu0yg4E.flac",
203
+ "prompt": "What is the quality of the breathing sounds?"
204
+ },
205
+ "output": {}
206
+ },
207
+ {
208
+ "id": "opqa_033",
209
+ "input": {
210
+ "audio_file": "575971.flac",
211
+ "prompt": "What is the predominant instrument in the loop?"
212
+ },
213
+ "output": {}
214
+ },
215
+ {
216
+ "id": "opqa_238",
217
+ "input": {
218
+ "audio_file": "j5oZYOBOppQ_000052.flac",
219
+ "prompt": "What are some possible reasons for a mouse to squeak?"
220
+ },
221
+ "output": {}
222
+ },
223
+ {
224
+ "id": "opqa_219",
225
+ "input": {
226
+ "audio_file": "159426.flac",
227
+ "prompt": "What are some potential impacts that the presence of crows and their calls in an urban setting might have on the surrounding environment and human population?"
228
+ },
229
+ "output": {}
230
+ },
231
+ {
232
+ "id": "opqa_063",
233
+ "input": {
234
+ "audio_file": "oNbekRS85f0_000003.flac",
235
+ "prompt": "What potential scenario could this audio clip be from based on the sound of the engine?"
236
+ },
237
+ "output": {}
238
+ },
239
+ {
240
+ "id": "opqa_123",
241
+ "input": {
242
+ "audio_file": "qEWBkrVGkng_000063.flac",
243
+ "prompt": "How does the acoustic feature of the pheasant crowing differ from other bird calls?"
244
+ },
245
+ "output": {}
246
+ },
247
+ {
248
+ "id": "opqa_377",
249
+ "input": {
250
+ "audio_file": "wind_turbine_blades.wav",
251
+ "prompt": "How does the combination of sound events in the audio clip create a certain atmosphere or mood?"
252
+ },
253
+ "output": {}
254
+ }
255
+ ]
256
+ }
Scope-A/S-A-Quick/audio/comprehension/SingerIdentification/annotation.json ADDED
@@ -0,0 +1,177 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "singer identification",
4
+ "data_source": "VocalSet",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data_description": "The VocalSet dataset is a dataset for singer identification. The dataset contains audio recordings of 5 different singers, i.e., female2, female8, male3, male5, male10.",
15
+ "data": [
16
+ {
17
+ "id": "si_352",
18
+ "input": {
19
+ "audio_file": "male10\\scales\\fast_forte\\m10_scales_c_fast_forte_a.wav",
20
+ "prompt": "who is the singer in the audio?"
21
+ },
22
+ "output": {}
23
+ },
24
+ {
25
+ "id": "si_101",
26
+ "input": {
27
+ "audio_file": "male10\\scales\\vocal_fry\\m10_scales_vocal_fry_a.wav",
28
+ "prompt": "who is the singer in the audio?"
29
+ },
30
+ "output": {}
31
+ },
32
+ {
33
+ "id": "si_406",
34
+ "input": {
35
+ "audio_file": "female8\\arpeggios\\fast_piano\\f8_arpeggios_f_fast_piano_u.wav",
36
+ "prompt": "who is the singer in the audio?"
37
+ },
38
+ "output": {}
39
+ },
40
+ {
41
+ "id": "si_333",
42
+ "input": {
43
+ "audio_file": "female2\\long_tones\\trillo\\f2_long_trillo_i.wav",
44
+ "prompt": "who is the singer in the audio?"
45
+ },
46
+ "output": {}
47
+ },
48
+ {
49
+ "id": "si_089",
50
+ "input": {
51
+ "audio_file": "female8\\arpeggios\\lip_trill\\f8_arpeggios_lip_trill_u.wav",
52
+ "prompt": "who is the singer in the audio?"
53
+ },
54
+ "output": {}
55
+ },
56
+ {
57
+ "id": "si_223",
58
+ "input": {
59
+ "audio_file": "male5\\scales\\slow_forte\\m5_scales_c_slow_forte_u.wav",
60
+ "prompt": "who is the singer in the audio?"
61
+ },
62
+ "output": {}
63
+ },
64
+ {
65
+ "id": "si_072",
66
+ "input": {
67
+ "audio_file": "male5\\long_tones\\straight\\m5_long_straight_e.wav",
68
+ "prompt": "who is the singer in the audio?"
69
+ },
70
+ "output": {}
71
+ },
72
+ {
73
+ "id": "si_481",
74
+ "input": {
75
+ "audio_file": "male3\\scales\\fast_forte\\m3_scales_c_fast_forte_e.wav",
76
+ "prompt": "who is the singer in the audio?"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "si_167",
82
+ "input": {
83
+ "audio_file": "male10\\scales\\slow_forte\\m10_scales_c_slow_forte_o.wav",
84
+ "prompt": "who is the singer in the audio?"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "si_364",
90
+ "input": {
91
+ "audio_file": "female2\\scales\\fast_piano\\f2_scales_f_fast_piano_a.wav",
92
+ "prompt": "who is the singer in the audio?"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "si_497",
98
+ "input": {
99
+ "audio_file": "male5\\arpeggios\\fast_forte\\m5_arpeggios_c_fast_forte_e.wav",
100
+ "prompt": "who is the singer in the audio?"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "si_409",
106
+ "input": {
107
+ "audio_file": "male5\\scales\\slow_piano\\m5_scales_f_slow_piano_i.wav",
108
+ "prompt": "who is the singer in the audio?"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "si_420",
114
+ "input": {
115
+ "audio_file": "male3\\arpeggios\\belt\\m3_arpeggios_belt_u.wav",
116
+ "prompt": "who is the singer in the audio?"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "si_079",
122
+ "input": {
123
+ "audio_file": "female2\\scales\\slow_piano\\f2_scales_f_slow_piano_u.wav",
124
+ "prompt": "who is the singer in the audio?"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "si_335",
130
+ "input": {
131
+ "audio_file": "female2\\long_tones\\forte\\f2_long_forte_e.wav",
132
+ "prompt": "who is the singer in the audio?"
133
+ },
134
+ "output": {}
135
+ },
136
+ {
137
+ "id": "si_368",
138
+ "input": {
139
+ "audio_file": "female8\\excerpts\\vibrato\\f8_dona_vibrato.wav",
140
+ "prompt": "who is the singer in the audio?"
141
+ },
142
+ "output": {}
143
+ },
144
+ {
145
+ "id": "si_109",
146
+ "input": {
147
+ "audio_file": "female8\\long_tones\\trill\\f8_long_trill_a.wav",
148
+ "prompt": "who is the singer in the audio?"
149
+ },
150
+ "output": {}
151
+ },
152
+ {
153
+ "id": "si_092",
154
+ "input": {
155
+ "audio_file": "male3\\long_tones\\trillo\\m3_long_trillo_i.wav",
156
+ "prompt": "who is the singer in the audio?"
157
+ },
158
+ "output": {}
159
+ },
160
+ {
161
+ "id": "si_022",
162
+ "input": {
163
+ "audio_file": "female2\\arpeggios\\vocal_fry\\f2_arpeggios_vocal_fry_i.wav",
164
+ "prompt": "who is the singer in the audio?"
165
+ },
166
+ "output": {}
167
+ },
168
+ {
169
+ "id": "si_110",
170
+ "input": {
171
+ "audio_file": "male5\\scales\\fast_piano\\m5_scales_c_fast_piano_i.wav",
172
+ "prompt": "who is the singer in the audio?"
173
+ },
174
+ "output": {}
175
+ }
176
+ ]
177
+ }
Scope-A/S-A-Quick/audio/comprehension/SoundEventRecognition/annotation.json ADDED
@@ -0,0 +1,136 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "sound event classification",
4
+ "data_source": "UrbanSound8K",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "sec_418",
17
+ "input": {
18
+ "audio_file": "fold8/204526-2-0-153.wav",
19
+ "prompt": "what is the sound in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "sec_219",
25
+ "input": {
26
+ "audio_file": "fold6/66000-9-0-0.wav",
27
+ "prompt": "what is the sound in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "sec_357",
33
+ "input": {
34
+ "audio_file": "fold6/132162-9-1-68.wav",
35
+ "prompt": "what is the sound in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "sec_338",
41
+ "input": {
42
+ "audio_file": "fold6/132021-7-0-3.wav",
43
+ "prompt": "what is the sound in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "sec_312",
49
+ "input": {
50
+ "audio_file": "fold6/135544-6-12-0.wav",
51
+ "prompt": "what is the sound in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "sec_194",
57
+ "input": {
58
+ "audio_file": "fold5/173993-3-0-25.wav",
59
+ "prompt": "what is the sound in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "sec_110",
65
+ "input": {
66
+ "audio_file": "fold9/145683-6-0-0.wav",
67
+ "prompt": "what is the sound in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "sec_163",
73
+ "input": {
74
+ "audio_file": "fold7/518-4-0-3.wav",
75
+ "prompt": "what is the sound in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "sec_078",
81
+ "input": {
82
+ "audio_file": "fold7/162728-1-0-0.wav",
83
+ "prompt": "what is the sound in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "sec_307",
89
+ "input": {
90
+ "audio_file": "fold2/74507-0-0-6.wav",
91
+ "prompt": "what is the sound in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "sec_039",
97
+ "input": {
98
+ "audio_file": "fold3/54067-2-0-70.wav",
99
+ "prompt": "what is the sound in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "sec_406",
105
+ "input": {
106
+ "audio_file": "fold9/185374-9-0-23.wav",
107
+ "prompt": "what is the sound in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "sec_246",
113
+ "input": {
114
+ "audio_file": "fold5/104998-7-17-8.wav",
115
+ "prompt": "what is the sound in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "sec_237",
121
+ "input": {
122
+ "audio_file": "fold2/34621-4-18-0.wav",
123
+ "prompt": "what is the sound in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "sec_220",
129
+ "input": {
130
+ "audio_file": "fold8/72015-2-0-4.wav",
131
+ "prompt": "what is the sound in the audio?"
132
+ },
133
+ "output": {}
134
+ }
135
+ ]
136
+ }
Scope-A/S-A-Quick/audio/comprehension/SpeakerIdentification/annotation.json ADDED
@@ -0,0 +1,257 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "speaker identification",
4
+ "data_source": "VoxCeleb",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data_description": "The VoxCeleb dataset is a large-scale speaker identification dataset. It contains over 1 million utterances for 7,000 speakers. The speaker ID is int(id_string[2:])-10001 .",
15
+ "data": [
16
+ {
17
+ "id": "si_00065",
18
+ "input": {
19
+ "audio_file": "id10277/SeXsHJ3fW1c/00001.wav",
20
+ "prompt": "who is the speaker in the audio?"
21
+ },
22
+ "output": {}
23
+ },
24
+ {
25
+ "id": "si_00262",
26
+ "input": {
27
+ "audio_file": "id10306/WOjNZFtRwDg/00005.wav",
28
+ "prompt": "who is the speaker in the audio?"
29
+ },
30
+ "output": {}
31
+ },
32
+ {
33
+ "id": "si_00271",
34
+ "input": {
35
+ "audio_file": "id10308/sLv-3TCu2fE/00005.wav",
36
+ "prompt": "who is the speaker in the audio?"
37
+ },
38
+ "output": {}
39
+ },
40
+ {
41
+ "id": "si_00152",
42
+ "input": {
43
+ "audio_file": "id10290/T1y64qFjv3M/00001.wav",
44
+ "prompt": "who is the speaker in the audio?"
45
+ },
46
+ "output": {}
47
+ },
48
+ {
49
+ "id": "si_00125",
50
+ "input": {
51
+ "audio_file": "id10286/PlJd6NVoigs/00003.wav",
52
+ "prompt": "who is the speaker in the audio?"
53
+ },
54
+ "output": {}
55
+ },
56
+ {
57
+ "id": "si_00212",
58
+ "input": {
59
+ "audio_file": "id10301/vhzF7dhGJzI/00005.wav",
60
+ "prompt": "who is the speaker in the audio?"
61
+ },
62
+ "output": {}
63
+ },
64
+ {
65
+ "id": "si_00037",
66
+ "input": {
67
+ "audio_file": "id10273/MqKFZOAwUsg/00002.wav",
68
+ "prompt": "who is the speaker in the audio?"
69
+ },
70
+ "output": {}
71
+ },
72
+ {
73
+ "id": "si_00024",
74
+ "input": {
75
+ "audio_file": "id10271/gO805KoL2RM/00006.wav",
76
+ "prompt": "who is the speaker in the audio?"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "si_00261",
82
+ "input": {
83
+ "audio_file": "id10306/WOjNZFtRwDg/00002.wav",
84
+ "prompt": "who is the speaker in the audio?"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "si_00238",
90
+ "input": {
91
+ "audio_file": "id10303/LKDWhdJQFco/00020.wav",
92
+ "prompt": "who is the speaker in the audio?"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "si_00082",
98
+ "input": {
99
+ "audio_file": "id10280/PQBBKBYGWgU/00006.wav",
100
+ "prompt": "who is the speaker in the audio?"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "si_00179",
106
+ "input": {
107
+ "audio_file": "id10295/nt7dNRvlEHE/00003.wav",
108
+ "prompt": "who is the speaker in the audio?"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "si_00268",
114
+ "input": {
115
+ "audio_file": "id10308/sLv-3TCu2fE/00006.wav",
116
+ "prompt": "who is the speaker in the audio?"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "si_00277",
122
+ "input": {
123
+ "audio_file": "id10309/GyJHpVQmcvc/00003.wav",
124
+ "prompt": "who is the speaker in the audio?"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "si_00240",
130
+ "input": {
131
+ "audio_file": "id10303/LKDWhdJQFco/00017.wav",
132
+ "prompt": "who is the speaker in the audio?"
133
+ },
134
+ "output": {}
135
+ },
136
+ {
137
+ "id": "si_00260",
138
+ "input": {
139
+ "audio_file": "id10306/WOjNZFtRwDg/00004.wav",
140
+ "prompt": "who is the speaker in the audio?"
141
+ },
142
+ "output": {}
143
+ },
144
+ {
145
+ "id": "si_00057",
146
+ "input": {
147
+ "audio_file": "id10276/aaJo0S1lnhY/00004.wav",
148
+ "prompt": "who is the speaker in the audio?"
149
+ },
150
+ "output": {}
151
+ },
152
+ {
153
+ "id": "si_00217",
154
+ "input": {
155
+ "audio_file": "id10302/ZAMRSlopMWA/00001.wav",
156
+ "prompt": "who is the speaker in the audio?"
157
+ },
158
+ "output": {}
159
+ },
160
+ {
161
+ "id": "si_00124",
162
+ "input": {
163
+ "audio_file": "id10286/PlJd6NVoigs/00006.wav",
164
+ "prompt": "who is the speaker in the audio?"
165
+ },
166
+ "output": {}
167
+ },
168
+ {
169
+ "id": "si_00092",
170
+ "input": {
171
+ "audio_file": "id10281/NHARUN9OhSo/00006.wav",
172
+ "prompt": "who is the speaker in the audio?"
173
+ },
174
+ "output": {}
175
+ },
176
+ {
177
+ "id": "si_00126",
178
+ "input": {
179
+ "audio_file": "id10286/PlJd6NVoigs/00004.wav",
180
+ "prompt": "who is the speaker in the audio?"
181
+ },
182
+ "output": {}
183
+ },
184
+ {
185
+ "id": "si_00225",
186
+ "input": {
187
+ "audio_file": "id10303/LKDWhdJQFco/00008.wav",
188
+ "prompt": "who is the speaker in the audio?"
189
+ },
190
+ "output": {}
191
+ },
192
+ {
193
+ "id": "si_00019",
194
+ "input": {
195
+ "audio_file": "id10270/x6uYqmx31kE/00016.wav",
196
+ "prompt": "who is the speaker in the audio?"
197
+ },
198
+ "output": {}
199
+ },
200
+ {
201
+ "id": "si_00069",
202
+ "input": {
203
+ "audio_file": "id10278/fvX1Vtx3sNo/00002.wav",
204
+ "prompt": "who is the speaker in the audio?"
205
+ },
206
+ "output": {}
207
+ },
208
+ {
209
+ "id": "si_00010",
210
+ "input": {
211
+ "audio_file": "id10270/x6uYqmx31kE/00014.wav",
212
+ "prompt": "who is the speaker in the audio?"
213
+ },
214
+ "output": {}
215
+ },
216
+ {
217
+ "id": "si_00221",
218
+ "input": {
219
+ "audio_file": "id10302/ZAMRSlopMWA/00004.wav",
220
+ "prompt": "who is the speaker in the audio?"
221
+ },
222
+ "output": {}
223
+ },
224
+ {
225
+ "id": "si_00086",
226
+ "input": {
227
+ "audio_file": "id10280/PQBBKBYGWgU/00001.wav",
228
+ "prompt": "who is the speaker in the audio?"
229
+ },
230
+ "output": {}
231
+ },
232
+ {
233
+ "id": "si_00236",
234
+ "input": {
235
+ "audio_file": "id10303/LKDWhdJQFco/00018.wav",
236
+ "prompt": "who is the speaker in the audio?"
237
+ },
238
+ "output": {}
239
+ },
240
+ {
241
+ "id": "si_00114",
242
+ "input": {
243
+ "audio_file": "id10284/YN4cTBWM-QE/00008.wav",
244
+ "prompt": "who is the speaker in the audio?"
245
+ },
246
+ "output": {}
247
+ },
248
+ {
249
+ "id": "si_00055",
250
+ "input": {
251
+ "audio_file": "id10276/aaJo0S1lnhY/00003.wav",
252
+ "prompt": "who is the speaker in the audio?"
253
+ },
254
+ "output": {}
255
+ }
256
+ ]
257
+ }
Scope-A/S-A-Quick/audio/comprehension/SpeechCommand/annotation.json ADDED
@@ -0,0 +1,176 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "speech command classification",
4
+ "data_source": "speech_command_0.02",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data": [
15
+ {
16
+ "id": "scc_218",
17
+ "input": {
18
+ "audio_file": "on\\5f01c798_nohash_1.wav",
19
+ "prompt": "what is the command in the audio?"
20
+ },
21
+ "output": {}
22
+ },
23
+ {
24
+ "id": "scc_388",
25
+ "input": {
26
+ "audio_file": "_unknown_\\zero_837a0f64_nohash_3.wav.wav",
27
+ "prompt": "what is the command in the audio?"
28
+ },
29
+ "output": {}
30
+ },
31
+ {
32
+ "id": "scc_304",
33
+ "input": {
34
+ "audio_file": "down\\b83c1acf_nohash_2.wav",
35
+ "prompt": "what is the command in the audio?"
36
+ },
37
+ "output": {}
38
+ },
39
+ {
40
+ "id": "scc_141",
41
+ "input": {
42
+ "audio_file": "down\\837a0f64_nohash_3.wav",
43
+ "prompt": "what is the command in the audio?"
44
+ },
45
+ "output": {}
46
+ },
47
+ {
48
+ "id": "scc_453",
49
+ "input": {
50
+ "audio_file": "down\\c7dc7278_nohash_4.wav",
51
+ "prompt": "what is the command in the audio?"
52
+ },
53
+ "output": {}
54
+ },
55
+ {
56
+ "id": "scc_165",
57
+ "input": {
58
+ "audio_file": "left\\837a0f64_nohash_4.wav",
59
+ "prompt": "what is the command in the audio?"
60
+ },
61
+ "output": {}
62
+ },
63
+ {
64
+ "id": "scc_462",
65
+ "input": {
66
+ "audio_file": "up\\893705bb_nohash_7.wav",
67
+ "prompt": "what is the command in the audio?"
68
+ },
69
+ "output": {}
70
+ },
71
+ {
72
+ "id": "scc_131",
73
+ "input": {
74
+ "audio_file": "left\\37dca74f_nohash_2.wav",
75
+ "prompt": "what is the command in the audio?"
76
+ },
77
+ "output": {}
78
+ },
79
+ {
80
+ "id": "scc_081",
81
+ "input": {
82
+ "audio_file": "on\\c9b5ff26_nohash_2.wav",
83
+ "prompt": "what is the command in the audio?"
84
+ },
85
+ "output": {}
86
+ },
87
+ {
88
+ "id": "scc_461",
89
+ "input": {
90
+ "audio_file": "go\\5c8af87a_nohash_1.wav",
91
+ "prompt": "what is the command in the audio?"
92
+ },
93
+ "output": {}
94
+ },
95
+ {
96
+ "id": "scc_289",
97
+ "input": {
98
+ "audio_file": "_unknown_\\learn_ca4d5368_nohash_0.wav.wav",
99
+ "prompt": "what is the command in the audio?"
100
+ },
101
+ "output": {}
102
+ },
103
+ {
104
+ "id": "scc_009",
105
+ "input": {
106
+ "audio_file": "yes\\6205088b_nohash_1.wav",
107
+ "prompt": "what is the command in the audio?"
108
+ },
109
+ "output": {}
110
+ },
111
+ {
112
+ "id": "scc_253",
113
+ "input": {
114
+ "audio_file": "up\\ea356919_nohash_2.wav",
115
+ "prompt": "what is the command in the audio?"
116
+ },
117
+ "output": {}
118
+ },
119
+ {
120
+ "id": "scc_222",
121
+ "input": {
122
+ "audio_file": "yes\\cd85758f_nohash_3.wav",
123
+ "prompt": "what is the command in the audio?"
124
+ },
125
+ "output": {}
126
+ },
127
+ {
128
+ "id": "scc_101",
129
+ "input": {
130
+ "audio_file": "stop\\18f8afd5_nohash_3.wav",
131
+ "prompt": "what is the command in the audio?"
132
+ },
133
+ "output": {}
134
+ },
135
+ {
136
+ "id": "scc_199",
137
+ "input": {
138
+ "audio_file": "go\\e1469561_nohash_0.wav",
139
+ "prompt": "what is the command in the audio?"
140
+ },
141
+ "output": {}
142
+ },
143
+ {
144
+ "id": "scc_266",
145
+ "input": {
146
+ "audio_file": "right\\9a7c1f83_nohash_0.wav",
147
+ "prompt": "what is the command in the audio?"
148
+ },
149
+ "output": {}
150
+ },
151
+ {
152
+ "id": "scc_347",
153
+ "input": {
154
+ "audio_file": "yes\\0cb74144_nohash_1.wav",
155
+ "prompt": "what is the command in the audio?"
156
+ },
157
+ "output": {}
158
+ },
159
+ {
160
+ "id": "scc_195",
161
+ "input": {
162
+ "audio_file": "right\\893705bb_nohash_0.wav",
163
+ "prompt": "what is the command in the audio?"
164
+ },
165
+ "output": {}
166
+ },
167
+ {
168
+ "id": "scc_305",
169
+ "input": {
170
+ "audio_file": "right\\03401e93_nohash_0.wav",
171
+ "prompt": "what is the command in the audio?"
172
+ },
173
+ "output": {}
174
+ }
175
+ ]
176
+ }
Scope-A/S-A-Quick/audio/comprehension/SpeechEmotionRecognition/annotation.json ADDED
@@ -0,0 +1,263 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "Speech Emotion Recognition",
4
+ "data_source": "IEMOCAP",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data_description": "The IEMOCAP dataset is a multimodal dataset that contains audio, video, and text data. The dataset contains emotional speech data from 10 actors. The emotions in the dataset are anger, happiness, sadness, and neutral.",
15
+ "emotion_label": {
16
+ "neu": 0,
17
+ "hap": 1,
18
+ "ang": 2,
19
+ "sad": 3
20
+ },
21
+ "data": [
22
+ {
23
+ "id": "ser_475",
24
+ "input": {
25
+ "audio_file": "Ses01F_impro07/Ses01F_impro07_M008.wav",
26
+ "prompt": "what is the emotion in the audio?"
27
+ },
28
+ "output": {}
29
+ },
30
+ {
31
+ "id": "ser_007",
32
+ "input": {
33
+ "audio_file": "Ses01M_impro03/Ses01M_impro03_F006.wav",
34
+ "prompt": "what is the emotion in the audio?"
35
+ },
36
+ "output": {}
37
+ },
38
+ {
39
+ "id": "ser_067",
40
+ "input": {
41
+ "audio_file": "Ses01F_script02_2/Ses01F_script02_2_F009.wav",
42
+ "prompt": "what is the emotion in the audio?"
43
+ },
44
+ "output": {}
45
+ },
46
+ {
47
+ "id": "ser_025",
48
+ "input": {
49
+ "audio_file": "Ses01M_impro05/Ses01M_impro05_M024.wav",
50
+ "prompt": "what is the emotion in the audio?"
51
+ },
52
+ "output": {}
53
+ },
54
+ {
55
+ "id": "ser_006",
56
+ "input": {
57
+ "audio_file": "Ses01F_impro06/Ses01F_impro06_F009.wav",
58
+ "prompt": "what is the emotion in the audio?"
59
+ },
60
+ "output": {}
61
+ },
62
+ {
63
+ "id": "ser_220",
64
+ "input": {
65
+ "audio_file": "Ses01F_impro03/Ses01F_impro03_M009.wav",
66
+ "prompt": "what is the emotion in the audio?"
67
+ },
68
+ "output": {}
69
+ },
70
+ {
71
+ "id": "ser_437",
72
+ "input": {
73
+ "audio_file": "Ses01F_impro06/Ses01F_impro06_M014.wav",
74
+ "prompt": "what is the emotion in the audio?"
75
+ },
76
+ "output": {}
77
+ },
78
+ {
79
+ "id": "ser_118",
80
+ "input": {
81
+ "audio_file": "Ses01M_impro03/Ses01M_impro03_M000.wav",
82
+ "prompt": "what is the emotion in the audio?"
83
+ },
84
+ "output": {}
85
+ },
86
+ {
87
+ "id": "ser_181",
88
+ "input": {
89
+ "audio_file": "Ses01F_script01_3/Ses01F_script01_3_M005.wav",
90
+ "prompt": "what is the emotion in the audio?"
91
+ },
92
+ "output": {}
93
+ },
94
+ {
95
+ "id": "ser_003",
96
+ "input": {
97
+ "audio_file": "Ses01M_impro05/Ses01M_impro05_F017.wav",
98
+ "prompt": "what is the emotion in the audio?"
99
+ },
100
+ "output": {}
101
+ },
102
+ {
103
+ "id": "ser_203",
104
+ "input": {
105
+ "audio_file": "Ses01M_script02_2/Ses01M_script02_2_F015.wav",
106
+ "prompt": "what is the emotion in the audio?"
107
+ },
108
+ "output": {}
109
+ },
110
+ {
111
+ "id": "ser_287",
112
+ "input": {
113
+ "audio_file": "Ses01M_script01_3/Ses01M_script01_3_M020.wav",
114
+ "prompt": "what is the emotion in the audio?"
115
+ },
116
+ "output": {}
117
+ },
118
+ {
119
+ "id": "ser_491",
120
+ "input": {
121
+ "audio_file": "Ses01F_impro04/Ses01F_impro04_M005.wav",
122
+ "prompt": "what is the emotion in the audio?"
123
+ },
124
+ "output": {}
125
+ },
126
+ {
127
+ "id": "ser_291",
128
+ "input": {
129
+ "audio_file": "Ses01M_script01_1/Ses01M_script01_1_F029.wav",
130
+ "prompt": "what is the emotion in the audio?"
131
+ },
132
+ "output": {}
133
+ },
134
+ {
135
+ "id": "ser_367",
136
+ "input": {
137
+ "audio_file": "Ses01M_impro07/Ses01M_impro07_F007.wav",
138
+ "prompt": "what is the emotion in the audio?"
139
+ },
140
+ "output": {}
141
+ },
142
+ {
143
+ "id": "ser_377",
144
+ "input": {
145
+ "audio_file": "Ses01M_script01_3/Ses01M_script01_3_F026.wav",
146
+ "prompt": "what is the emotion in the audio?"
147
+ },
148
+ "output": {}
149
+ },
150
+ {
151
+ "id": "ser_286",
152
+ "input": {
153
+ "audio_file": "Ses01F_impro06/Ses01F_impro06_F007.wav",
154
+ "prompt": "what is the emotion in the audio?"
155
+ },
156
+ "output": {}
157
+ },
158
+ {
159
+ "id": "ser_199",
160
+ "input": {
161
+ "audio_file": "Ses01M_impro04/Ses01M_impro04_F003.wav",
162
+ "prompt": "what is the emotion in the audio?"
163
+ },
164
+ "output": {}
165
+ },
166
+ {
167
+ "id": "ser_055",
168
+ "input": {
169
+ "audio_file": "Ses01M_script01_2/Ses01M_script01_2_F001.wav",
170
+ "prompt": "what is the emotion in the audio?"
171
+ },
172
+ "output": {}
173
+ },
174
+ {
175
+ "id": "ser_015",
176
+ "input": {
177
+ "audio_file": "Ses01F_script02_2/Ses01F_script02_2_F001.wav",
178
+ "prompt": "what is the emotion in the audio?"
179
+ },
180
+ "output": {}
181
+ },
182
+ {
183
+ "id": "ser_274",
184
+ "input": {
185
+ "audio_file": "Ses01M_script02_2/Ses01M_script02_2_M043.wav",
186
+ "prompt": "what is the emotion in the audio?"
187
+ },
188
+ "output": {}
189
+ },
190
+ {
191
+ "id": "ser_485",
192
+ "input": {
193
+ "audio_file": "Ses01F_impro05/Ses01F_impro05_M001.wav",
194
+ "prompt": "what is the emotion in the audio?"
195
+ },
196
+ "output": {}
197
+ },
198
+ {
199
+ "id": "ser_030",
200
+ "input": {
201
+ "audio_file": "Ses01F_script01_1/Ses01F_script01_1_M019.wav",
202
+ "prompt": "what is the emotion in the audio?"
203
+ },
204
+ "output": {}
205
+ },
206
+ {
207
+ "id": "ser_057",
208
+ "input": {
209
+ "audio_file": "Ses01F_script01_2/Ses01F_script01_2_M008.wav",
210
+ "prompt": "what is the emotion in the audio?"
211
+ },
212
+ "output": {}
213
+ },
214
+ {
215
+ "id": "ser_266",
216
+ "input": {
217
+ "audio_file": "Ses01F_impro03/Ses01F_impro03_M011.wav",
218
+ "prompt": "what is the emotion in the audio?"
219
+ },
220
+ "output": {}
221
+ },
222
+ {
223
+ "id": "ser_126",
224
+ "input": {
225
+ "audio_file": "Ses01M_script03_2/Ses01M_script03_2_F028.wav",
226
+ "prompt": "what is the emotion in the audio?"
227
+ },
228
+ "output": {}
229
+ },
230
+ {
231
+ "id": "ser_467",
232
+ "input": {
233
+ "audio_file": "Ses01F_script02_2/Ses01F_script02_2_F042.wav",
234
+ "prompt": "what is the emotion in the audio?"
235
+ },
236
+ "output": {}
237
+ },
238
+ {
239
+ "id": "ser_369",
240
+ "input": {
241
+ "audio_file": "Ses01M_impro02/Ses01M_impro02_F017.wav",
242
+ "prompt": "what is the emotion in the audio?"
243
+ },
244
+ "output": {}
245
+ },
246
+ {
247
+ "id": "ser_391",
248
+ "input": {
249
+ "audio_file": "Ses01M_impro06/Ses01M_impro06_F027.wav",
250
+ "prompt": "what is the emotion in the audio?"
251
+ },
252
+ "output": {}
253
+ },
254
+ {
255
+ "id": "ser_416",
256
+ "input": {
257
+ "audio_file": "Ses01F_impro04/Ses01F_impro04_F001.wav",
258
+ "prompt": "what is the emotion in the audio?"
259
+ },
260
+ "output": {}
261
+ }
262
+ ]
263
+ }
Scope-A/S-A-Quick/audio/comprehension/SpeechEventExtraction/annotation.json ADDED
@@ -0,0 +1,177 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "set_type": "closeset",
3
+ "task": "speech event extraction",
4
+ "data_source": "SpeechEventExtraction",
5
+ "type": "audio comprehension",
6
+ "modality": {
7
+ "in": [
8
+ "audio",
9
+ "text"
10
+ ],
11
+ "out": "text"
12
+ },
13
+ "version": "1.0",
14
+ "data_description": "The SpeechEventExtraction dataset is a dataset for speech event extraction. The dataset contains audio recordings of speech events. The task is to extract the speech events in the audio.",
15
+ "data": [
16
+ {
17
+ "id": "see_00369",
18
+ "input": {
19
+ "audio_file": "train-369.wav",
20
+ "prompt": "extract the speech events in the given audio"
21
+ },
22
+ "output": {}
23
+ },
24
+ {
25
+ "id": "see_00352",
26
+ "input": {
27
+ "audio_file": "train-352.wav",
28
+ "prompt": "extract the speech events in the given audio"
29
+ },
30
+ "output": {}
31
+ },
32
+ {
33
+ "id": "see_00447",
34
+ "input": {
35
+ "audio_file": "train-447.wav",
36
+ "prompt": "extract the speech events in the given audio"
37
+ },
38
+ "output": {}
39
+ },
40
+ {
41
+ "id": "see_00428",
42
+ "input": {
43
+ "audio_file": "train-428.wav",
44
+ "prompt": "extract the speech events in the given audio"
45
+ },
46
+ "output": {}
47
+ },
48
+ {
49
+ "id": "see_00222",
50
+ "input": {
51
+ "audio_file": "train-222.wav",
52
+ "prompt": "extract the speech events in the given audio"
53
+ },
54
+ "output": {}
55
+ },
56
+ {
57
+ "id": "see_00320",
58
+ "input": {
59
+ "audio_file": "train-320.wav",
60
+ "prompt": "extract the speech events in the given audio"
61
+ },
62
+ "output": {}
63
+ },
64
+ {
65
+ "id": "see_00153",
66
+ "input": {
67
+ "audio_file": "train-153.wav",
68
+ "prompt": "extract the speech events in the given audio"
69
+ },
70
+ "output": {}
71
+ },
72
+ {
73
+ "id": "see_00244",
74
+ "input": {
75
+ "audio_file": "train-244.wav",
76
+ "prompt": "extract the speech events in the given audio"
77
+ },
78
+ "output": {}
79
+ },
80
+ {
81
+ "id": "see_00026",
82
+ "input": {
83
+ "audio_file": "train-26.wav",
84
+ "prompt": "extract the speech events in the given audio"
85
+ },
86
+ "output": {}
87
+ },
88
+ {
89
+ "id": "see_00493",
90
+ "input": {
91
+ "audio_file": "train-493.wav",
92
+ "prompt": "extract the speech events in the given audio"
93
+ },
94
+ "output": {}
95
+ },
96
+ {
97
+ "id": "see_00194",
98
+ "input": {
99
+ "audio_file": "train-194.wav",
100
+ "prompt": "extract the speech events in the given audio"
101
+ },
102
+ "output": {}
103
+ },
104
+ {
105
+ "id": "see_00006",
106
+ "input": {
107
+ "audio_file": "train-6.wav",
108
+ "prompt": "extract the speech events in the given audio"
109
+ },
110
+ "output": {}
111
+ },
112
+ {
113
+ "id": "see_00036",
114
+ "input": {
115
+ "audio_file": "train-36.wav",
116
+ "prompt": "extract the speech events in the given audio"
117
+ },
118
+ "output": {}
119
+ },
120
+ {
121
+ "id": "see_00380",
122
+ "input": {
123
+ "audio_file": "train-380.wav",
124
+ "prompt": "extract the speech events in the given audio"
125
+ },
126
+ "output": {}
127
+ },
128
+ {
129
+ "id": "see_00143",
130
+ "input": {
131
+ "audio_file": "train-143.wav",
132
+ "prompt": "extract the speech events in the given audio"
133
+ },
134
+ "output": {}
135
+ },
136
+ {
137
+ "id": "see_00094",
138
+ "input": {
139
+ "audio_file": "train-94.wav",
140
+ "prompt": "extract the speech events in the given audio"
141
+ },
142
+ "output": {}
143
+ },
144
+ {
145
+ "id": "see_00218",
146
+ "input": {
147
+ "audio_file": "train-218.wav",
148
+ "prompt": "extract the speech events in the given audio"
149
+ },
150
+ "output": {}
151
+ },
152
+ {
153
+ "id": "see_00478",
154
+ "input": {
155
+ "audio_file": "train-478.wav",
156
+ "prompt": "extract the speech events in the given audio"
157
+ },
158
+ "output": {}
159
+ },
160
+ {
161
+ "id": "see_00396",
162
+ "input": {
163
+ "audio_file": "train-396.wav",
164
+ "prompt": "extract the speech events in the given audio"
165
+ },
166
+ "output": {}
167
+ },
168
+ {
169
+ "id": "see_00275",
170
+ "input": {
171
+ "audio_file": "train-275.wav",
172
+ "prompt": "extract the speech events in the given audio"
173
+ },
174
+ "output": {}
175
+ }
176
+ ]
177
+ }