File size: 1,575 Bytes
107faba c0b2093 107faba c0b2093 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
---
license: cc-by-sa-4.0
task_categories:
- image-text-to-text
---
# Visual-RAG-ME
[**Project Page**](https://xiaowu0162.github.io/visret/) | [**Paper**](https://huggingface.co/papers/2505.20291) | [**GitHub**](https://github.com/xiaowu0162/visualize-then-retrieve)
Official data for **Visual-RAG-ME**, a benchmark for multi-entity text-to-image retrieval and visual question answering (VQA). This dataset was introduced in the paper [VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval](https://huggingface.co/papers/2505.20291).
## Dataset Description
Visual-RAG-ME is a new benchmark annotated for comparing features across related organisms. It is designed to evaluate models on two primary tasks:
1. **Multi-entity Text-to-Image Retrieval**: Navigating structured visual relationships such as pose and viewpoint in knowledge-intensive scenarios.
2. **Visual Question Answering (VQA)**: Assessing the model's ability to answer questions based on retrieved visual information.
The benchmark highlights the limitations of traditional cross-modal similarity alignment and supports the **Visualize-then-Retrieve (VisRet)** paradigm, which improves retrieval by projecting textual queries into the image modality via generation.
## Citation
If you find this dataset useful, please cite the following paper:
```bibtex
@article{wu2025visret,
title={VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval},
author={Wu, Di and Wan, Yixin and Chang, Kai-Wei},
journal={arXiv preprint arXiv:2505.20291},
year={2025}
}
``` |