README.md

---
license: mit
---

The dataset used to train and evaluate [ReT](https://www.arxiv.org/abs/2503.01980) for multimodal information retrieval. The dataset is almost the same as the original [M2KR](https://huggingface.co/datasets/BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR), with a few modifications:
- we exlude any data from MSMARCO, as it does not contain query images;
- we add passage images to OVEN, InfoSeek, E-VQA, and OKVQA. Refer to the paper for more details.


## Sources
- **Repository:** https://github.com/aimagelab/ReT
- **Paper:** [Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval](https://www.arxiv.org/abs/2503.01980) (CVPR 2025)


## Download images
1. Initialize git LFS
```
git lfs install
```

2. Clone the repository (it will take a lot)
```
git clone https://huggingface.co/datasets/aimagelab/ReT-M2KR
```

3. Decompress images (it will take a lot, again)
```
cat ret-img-{000..129}.tar.gz | tar xzf -
```


## RAG - InfoSeek
`jsonl/rag/kb_infoseek50k.jsonl` is the knowledge base used to execute experiments on Retrieval-Augmented Generation on the InfoSeek benchmark. The field `passage_image_path` contains a relative path to the Wikipedia image  associated with a given passage. The Wikipedia images can be downloaded from the [OVEN](https://huggingface.co/datasets/ychenNLP/oven/blob/main/all_wikipedia_images.tar) repository.  

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{caffagni2025recurrence,
  title={{Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval}},
  author={Caffagni, Davide and Sarto, Sara and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2025}
}
```