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README.md

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-license: cc-by-4.0
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+# 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:**
+```json
+  {
+    "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.
+
+```python
+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 [email protected]. 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](https://creativecommons.org/licenses/by/4.0/)  
+- Images: Subject to custom terms (see Disclaimer above).
+
+## Citation
+
+**BibTeX:**
+```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}
+}
+```