rootsautomation/RICO-WidgetCaptioning

Dataset Card for RICO Widget Captioning

Widget Captioning is a dataset for providing captions for UI elements on mobile screens. It uses the RICO image database.

Dataset Details

Dataset Sources

• Repository:
  • google-research-datasets/widget-caption
  • RICO raw downloads
• Paper:
  • Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
  • Rico: A Mobile App Dataset for Building Data-Driven Design Applications

Uses

This dataset is for developing multimodal automations for mobile screens.

Direct Use

• Enhancing screen readers
• Screen indexing
• Conversational mobile applications
• Q&A on screens

Dataset Structure

• screenId: Unique RICO screen ID
• image: RICO screenshot
• image_icon: Google Play Store icon for the app
• image_semantic: Semantic RICO screenshot; details are abstracted away to main visual UI elements
• file_name: Image local filename
• file_name_icon: Icon image local filename
• file_name_semantic: Screenshot Image as a semantic annotated image local filename
• captions: A list of string captions
• bbox: The bounding box for the widget being captioned, relatively scaled with the image size so that coordinates are in [0, 1]
• app_package_name: Android package name
• play_store_name: Google Play Store name
• category: Type of category of the app
• number_of_downloads: Number of downloads of the app (as a coarse range string)
• number_of_ratings: Number of ratings of the app on the Google Play store (as of collection)
• average_rating: Average rating of the app on the Google Play Store (as of collection)
• semantic_annotations: Reduced view hierarchy, to the semantically-relevant portions of the full view hierarchy. It corresponds to what is visualized in image_semantic and has a lot of details about what's on screen. It is stored as a JSON object string.

Dataset Creation

Curation Rationale

• RICO rationale: Create a broad dataset that can be used for UI automation. An explicit goal was to develop automation software that can validate an app's design and assess whether it achieves its stated goal.
• Widget Captioning rationale: Create a dataset that helps machines reason about UI elements on screens

Source Data

• RICO: Mobile app screenshots, collected on Android devices.
• Widget Captioning: Human annotated concise captions for widgets on screen

Data Collection and Processing

• RICO: Human and automated collection of Android screens. ~9.8k free apps from the Google Play Store.
• Widget Captioning: Takes the subset of screens used in RICO, eliminates screens with missing or inaccurate view hierarchies.

Who are the source data producers?

• RICO: 13 human workers (10 from the US, 3 from the Philippines) through UpWork.
• Widget Captioning: 5.4k annotators through Amazon Mechanical Turk

Citation

RICO

BibTeX:

@inproceedings{deka2017rico,
  title={Rico: A mobile app dataset for building data-driven design applications},
  author={Deka, Biplab and Huang, Zifeng and Franzen, Chad and Hibschman, Joshua and Afergan, Daniel and Li, Yang and Nichols, Jeffrey and Kumar, Ranjitha},
  booktitle={Proceedings of the 30th annual ACM symposium on user interface software and technology},
  pages={845--854},
  year={2017}
}

APA:

Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., ... & Kumar, R. (2017, October). Rico: A mobile app dataset for building data-driven design applications. In Proceedings of the 30th annual ACM symposium on user interface software and technology (pp. 845-854).

Widget Captioning

BibTeX:

@inproceedings{li2020widget,
  title={Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements},
  author={Li, Yang and Li, Gang and He, Luheng and Zheng, Jingjie and Li, Hong and Guan, Zhiwei},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={5495--5510},
  year={2020}
}

APA:

Li, Y., Li, G., He, L., Zheng, J., Li, H., & Guan, Z. (2020, November). Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5495-5510).

Dataset Card Authors

Hunter Heidenreich, Roots Automation

Dataset Card Contact

hunter "DOT" heidenreich "AT" rootsautomation "DOT" com