README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: int64
    - name: multimodal_question
      dtype: string
    - name: answer
      dtype: string
    - name: rationale
      dtype: string
    - name: text_only_question
      dtype: string
    - name: image_source
      dtype: string
    - name: evidence
      dtype: string
    - name: resolution
      dtype: string
    - name: proportion_of_roi
      dtype: string
    - name: category
      dtype: string
    - name: text_in_image
      dtype: string
    - name: rationale_granularity
      dtype: string
    - name: image
      dtype: image
    - name: cropped_image
      dtype: image
  splits:
    - name: train
      num_bytes: 157153160
      num_examples: 129
  download_size: 157133331
  dataset_size: 157153160
configs:
  - config_name: default
    data_files:
      - split: train
        path: hard_data/train-*

VisualSimpleQA

Introduction

VisualSimpleQA is a multimodal fact-seeking benchmark with two key features. First, it enables streamlined and decoupled evaluation of LVLMs in visual and linguistic modalities. Second, it incorporates well-defined difficulty criteria to guide human annotation and facilitates the extraction of a challenging subset, VisualSimpleQA-hard. Experiments on 15 LVLMs show that even state-of-the-art models such as GPT-4o achieve merely 60%+ correctness in multimodal fact-seeking QA on VisualSimpleQA and 30%+ on VisualSimpleQA-hard. Furthermore, the decoupled evaluation based on this benchmark across different models highlights substantial opportunities for improvement in both visual and linguistic modules.

Structure

original_image/ This directory contains all image files, where each filename follows the format original_image_{ID}.png, matching the unique ID of the corresponding sample in VisualSimpleQA.

cropped_image/ This directory contains all cropped rationales from the original images. Each filename follows the format cropped_image_{ID}.painting, matching the unique ID of the corresponding sample in VisualSimpleQA.

data.json This JSON file provides detailed information about each sample.

hard_data.json This JSON file provides detailed information about each hard sample in the following format:

Example:

  {
    "id": 369,
    "multimodal_question": "Which institution did the creator of this cartoon duck donate her natural science-related paintings to?",
    "answer": "The Armitt Museum, Gallery, Library",
    "rationale": "Jemima Puddle-Duck",
    "text_only_question": "Which institution did the creator of Jemima Puddle-Duck donate her natural science-related paintings to?",
    "image_source": "https://www.gutenberg.org/files/14814/14814-h/images/15-tb.jpg",
    "evidence": "https://www.armitt.com/beatrix-potter-exhibition/\nhttps://en.wikipedia.org/wiki/Beatrix_Potter",
    "resolution": "400x360",
    "proportion_of_roi": 0.2232,
    "category": "academic and education",
    "text_in_image": "absence",
    "rationale_granularity": "fine-grained"
  }

Usage

1. Ensure the data.json file is in the same directory as the script.
2. Run the script to randomly select and display a sample.

import json
import random

with open('./data.json', 'r', encoding='utf-8') as file:
    data = json.load(file)

# Randomly select one sample
random_sample = random.choice(data)
image_id = random_sample.get('id')
image_path = f'./original_image/original_image_{image_id}.png'
multimodal_question = random_sample.get('multimodal_question')
text_only_question = random_sample.get('text_only_question')
answer = random_sample.get('answer')

Disclaimer

This dataset contains images collected from various sources. The authors do NOT claim ownership or copyright over the images. The images may be subject to third-party rights, and users are solely responsible for verifying the legal status of any content before use.

• Intended Use: The images are provided for ​non-commercial research purposes only.

• Redistribution Prohibition: You may NOT redistribute or modify the images without permission from original rights holders.

• Reporting Violations: If you encounter any sample potentially breaching copyright or licensing rules, contact us at yanlingwang777@gmail.com. Verified violations will be removed promptly.

The authors disclaim all liability for copyright infringement or misuse arising from the use of this dataset. Users assume full legal responsibility for their actions.

License

• Text Data: Licensed under CC-BY-4.0
• Images: Subject to custom terms (see Disclaimer above).

Citation

BibTeX:

@article{wang2025visualsimpleqa,
  title={VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering},
  author={Yanling Wang and Yihan Zhao and Xiaodong Chen and Shasha Guo and Lixin Liu and Haoyang Li and Yong Xiao and Jing Zhang and Qi Li and Ke Xu},
  journal={arXiv preprint arXiv:},
  year={2025}
}