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license: mit
language:
  - en

Vis-IR: Unifying Search With Visualized Information Retrieval

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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.

image/png

Organization Structure

The dataset is organized in the following structure:

Domain/  
├── caption.jsonl: a screenshot image path and its corresponding caption 
├── q2s.jsonl: a query, a positive screenshot and eight negative screenshots
├── sq2s.jsonl: a query, a query screenshot, a positive screenshot and eight negative screenshots
├── Domain.tar.gz.partaa 
├── Domain.tar.gz.partab
├── Domain.tar.gz.partac
...

Download Images

We have released all the images. Due to the large total size, they are split into multiple parts. You can download the images using the script below:

#!/bin/bash

FOLDER="Chart" # “News” "PDFA" "Papers" "Product" "Readmes" "Wiki"
BASE_URL="https://huggingface.co/datasets/marsh123/VIRA/resolve/main/${FOLDER}"

mkdir -p "$FOLDER"
cd "$FOLDER"

for first in {a..z}; do
  for second in {a..z}; do
    part="${first}${second}"
    file="${FOLDER}.tar.gz.part${part}"
    url="${BASE_URL}/${file}"

    echo "Trying: $url"
    if curl --output /dev/null --silent --head --fail "$url"; then
      wget --continue "$url"
    else
      echo "No more parts after part${part}"
      break 2  
    fi
  done
done

echo "Merging parts..."
cat "${FOLDER}.tar.gz.part"* > "${FOLDER}.tar.gz"

echo "Extracting..."
tar -xzvf "${FOLDER}.tar.gz"

echo "Cleaning up..."
rm "${FOLDER}.tar.gz.part"*
rm "${FOLDER}.tar.gz"

echo "✅ Done!"

License

VIRA is licensed under the MIT 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}
}