Add comprehensive dataset card for MM-RIS (#2)

- Add comprehensive dataset card for MM-RIS (ef37cca438be6cdf678ea78d702c0aab3ef1cfe3)


Co-authored-by: Niels Rogge <[email protected]>

README.md

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+---
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- multimodal
+- referring-image-segmentation
+- infrared
+- visible
+- image-fusion
+size_categories:
+- 10K<n<100K
+---
+
+# MM-RIS: Multimodal Referring Image Segmentation Dataset
+
+The **MM-RIS** dataset was introduced in the paper [RIS-FUSION: Rethinking Text-Driven Infrared and Visible Image Fusion from the Perspective of Referring Image Segmentation](https://huggingface.co/papers/2509.12710).
+
+This large-scale benchmark supports the multimodal referring image segmentation (RIS) task by providing a goal-aligned approach to supervise and evaluate how effectively natural language contributes to infrared and visible image fusion outcomes.
+
+## Paper
+
+[RIS-FUSION: Rethinking Text-Driven Infrared and Visible Image Fusion from the Perspective of Referring Image Segmentation](https://huggingface.co/papers/2509.12710)
+
+## Code
+
+The official code repository for the associated RIS-FUSION project can be found on GitHub: [https://github.com/SijuMa2003/RIS-FUSION](https://github.com/SijuMa2003/RIS-FUSION)
+
+## Introduction
+
+Text-driven infrared and visible image fusion has gained attention for enabling natural language to guide the fusion process. However, existing methods often lack a goal-aligned task to supervise and evaluate how effectively the input text contributes to the fusion outcome.
+
+We observe that **referring image segmentation (RIS)** and text-driven fusion share a common objective: highlighting the object referred to by the text. Motivated by this, we propose **RIS-FUSION**, a cascaded framework that unifies fusion and RIS through joint optimization.
+
+To support the multimodal referring image segmentation task, we introduce **MM-RIS**, a large-scale benchmark with **12.5k training** and **3.5k testing** triplets, each consisting of an infrared-visible image pair, a segmentation mask, and a referring expression.
+
+## Dataset Structure
+
+The MM-RIS dataset is available in this Hugging Face repository and consists of the following Parquet files:
+
+- `mm_ris_test.parquet`
+- `mm_ris_val.parquet`
+- `mm_ris_train_part1.parquet`
+- `mm_ris_train_part2.parquet`
+
+These files together comprise 12.5k training and 3.5k testing triplets. Each triplet includes an infrared image, a visible image, a segmentation mask, and a natural language referring expression.
+
+## Sample Usage
+
+To prepare the MM-RIS dataset for use with the RIS-FUSION code, you will need to download all the dataset files from this repository and merge the training partitions.
+
+1.  **Download the dataset files**:
+    Download `mm_ris_test.parquet`, `mm_ris_val.parquet`, `mm_ris_train_part1.parquet`, and `mm_ris_train_part2.parquet` from this Hugging Face repository and place them under a `data/` directory in your project, ideally within a cloned RIS-FUSION GitHub repository.
+
+2.  **Merge partitioned parquet files**:
+    The RIS-FUSION GitHub repository provides a script to merge the partitioned training data. Assuming you have cloned the repository and placed the parquet files in `./data/`:
+
+    ```bash
+    python ./data/merge_parquet.py
+    ```
+    This script will combine `mm_ris_train_part1.parquet` and `mm_ris_train_part2.parquet` into a single `mm_ris_train.parquet` file.
+
+Once the dataset is prepared, you can use it for training and testing models as shown in the examples below.
+
+### Training Example
+
+```bash
+python train_with_lavt.py      \
+  --train_parquet ./data/mm_ris_train.parquet    \
+  --val_parquet   ./data/mm_ris_val.parquet     \
+  --prefusion_model unet_fuser --prefusion_base_ch 32     \
+  --epochs 10 -b 16 -j 16     \
+  --img_size 480     \
+  --swin_type base \
+  --pretrained_swin_weights ./pretrained_weights/swin_base_patch4_window12_384_22k.pth     \
+  --bert_tokenizer ./bert/pretrained_weights/bert-base-uncased \
+  --ck_bert ./bert/pretrained_weights/bert-base-uncased     \
+  --init_from_lavt_one ./pretrained_weights/lavt_one_8_cards_ImgNet22KPre_swin-base-window12_refcoco+_adamw_b32lr0.00005wd1e-2_E40.pth     \
+  --lr_seg 5e-5 --wd_seg 1e-2 --lr_pf 1e-4 --wd_pf 1e-2     \
+  --lambda_prefusion 3.0     \
+  --w_sobel_vis 0.0 \
+  --w_sobel_ir 1.0     \
+  --w_grad 1.0     \
+  --w_ssim_vis 0.5 \
+  --w_ssim_ir 0.0     \
+  --w_mse_vis 0.5 \
+  --w_mse_ir 2.0     
+  --eval_vis_dir ./eval_vis \
+  --output-dir ./ckpts/risfusion
+```
+
+### Testing Example
+
+```bash
+python test.py   \
+  --ckpt  ./ckpts/risfusion/model_best_lavt.pth   \
+  --test_parquet ./data/mm_ris_test.parquet   \
+  --out_dir ./your_output_dir  \
+  --bert_tokenizer ./bert/pretrained_weights/bert-base-uncased   \
+  --ck_bert ./bert/pretrained_weights/bert-base-uncased
+```
+
+## Citation
+
+If you find this dataset or the associated paper useful, please consider citing:
+
+```bibtex
+@article{RIS-FUSION2025,
+  title   = {RIS-FUSION: Rethinking Text-Driven Infrared and Visible Image Fusion from the Perspective of Referring Image Segmentation},
+  author  = {Ma, Siju and Gong, Changsiyu and Fan, Xiaofeng and Ma, Yong and Jiang, Chengjie},
+  journal = {...},
+  year    = {2025}
+}
+```
+
+## Acknowledgements
+
+-   [Swin Transformer](https://github.com/microsoft/Swin-Transformer)
+-   [LAVT](https://github.com/yz93/LAVT)
+-   [MMEngine](https://github.com/open-mmlab/mmengine)