Datasets:
Tasks:
Image Segmentation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
File size: 5,230 Bytes
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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) |