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---
license: mit
language:
- en
---
<h1 align="center">Vis-IR: Unifying Search With Visualized Information Retrieval</h1>
<p align="center">
<a href="https://arxiv.org/abs/2502.11431">
<img alt="Build" src="http://img.shields.io/badge/arXiv-2502.11431-B31B1B.svg">
</a>
<a href="https://github.com/VectorSpaceLab/Vis-IR">
<img alt="Build" src="https://img.shields.io/badge/Github-Code-blue">
</a>
<a href="https://huggingface.co/datasets/marsh123/VIRA/">
<img alt="Build" src="https://img.shields.io/badge/π€ Datasets-VIRA-yellow">
</a>
<a href="https://huggingface.co/datasets/marsh123/MVRB">
<img alt="Build" src="https://img.shields.io/badge/π€ Datasets-MVRB-yellow">
</a>
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<img alt="Build" src="https://img.shields.io/badge/π€ Model-UniSE CLIP-yellow">
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<a href="https://huggingface.co/marsh123/UniSE">
<img alt="Build" src="https://img.shields.io/badge/π€ Model-UniSE MLLM-yellow">
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</p>
## Overview
**VIRA** (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and questionanswer formats.
## Statistics
There are three types of data in VIRA: caption data, query-to-screenshot (q2s) data, and screenshot+query-to-screenshot (sq2s) data. The table below provides a detailed breakdown of the data counts for each domain and type.

## Organization Structure
The dataset is organized in the following structure:
```tree
Domain/
βββ caption.jsonl: a screenshot image path and its corresponding caption
βββ q2s.jsonl: a query, a positive screenshot and eight negative screenshots
βββ sq2s.jsonl: a query, a query screenshot, a positive screenshot and eight negative screenshots
βββ images/
βββ image1.jpg
βββ image2.jpg
...
```
_Due to the large number of images, uploading all of them takes time. Currently, the upload is not yet complete, and we will continue the process._
## License
VIRA is licensed under the [MIT License](LICENSE).
## Citation
If you find this dataset useful, please cite:
```
@article{liu2025any,
title={Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval},
author={Liu, Ze and Liang, Zhengyang and Zhou, Junjie and Liu, Zheng and Lian, Defu},
journal={arXiv preprint arXiv:2502.11431},
year={2025}
}
```
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