README.md

metadata

license: mit

The dataset used to train and evaluate ReT for multimodal information retrieval. The dataset is almost the same as the original 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 (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 repository.

Citation

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}
}