Add metadata, paper/GitHub links, and dataset description

README.md

@@ -1,5 +1,32 @@
 ---
 license: cc-by-sa-4.0
+task_categories:
+- image-text-to-text
 ---
 
-Official data for Visual-RAG-ME, a multi-entity text-to-image retrieval and VQA dataset. Please check [our paper](https://arxiv.org/pdf/2505.20291) for more details.
+# 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}
+}
+```