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--- |
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license: mit |
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task_categories: |
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- question-answering |
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size_categories: |
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- 10M<n<100M |
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--- |
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# 🧠 EgoExoBench: A Cross-Perspective Video Understanding Benchmark |
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## Dataset Summary |
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EgoExoBench is a benchmark designed to evaluate **cross-perspective understanding** capabilities of multimodal large models (MLLMs). |
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It contains synchronized and asynchronous **egocentric (first-person)** and **exocentric (third-person)** video pairs, along with multiple-choice questions that assess semantic alignment, viewpoint association, and temporal reasoning between the two perspectives. |
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## Features |
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Each sample contains: |
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- **Question**: A natural-language question testing cross-perspective reasoning. |
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- **Options**: Multiple-choice answers (A/B/C/D). |
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- **Answer**: Correct answer label. |
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- **Videos**: Egocentric and Exocentric videos. |
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## Evaluation Metric |
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Accuracy (%) |
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## Data Splits |
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| Split | #Samples | |
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|--------|-----------| |
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| Test | 7,330 | |
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## Example Usage |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("YourUsername/EgoExoBench") |
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print(dataset["test"][0]) |
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``` |
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## Citation |
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If you use EgoExoBench in your research, please cite: |
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``` |
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@misc{he2025egoexobench, |
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title={EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMs}, |
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author={Yuping He and Yifei Huang and Guo Chen and Baoqi Pei and Jilan Xu and Tong Lu and Jiangmiao Pang}, |
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year={2025}, |
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eprint={2507.18342}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2507.18342} |
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} |
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``` |
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