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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.05787 |
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| _version_ | 1866908478291836928 |
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| author | Rodin, Ivan Wu, Tz-Ying Min, Kyle Sridhar, Sharath Nittur Furnari, Antonino Tripathi, Subarna Farinella, Giovanni Maria |
| author_facet | Rodin, Ivan Wu, Tz-Ying Min, Kyle Sridhar, Sharath Nittur Furnari, Antonino Tripathi, Subarna Farinella, Giovanni Maria |
| contents | We introduce EASG-Bench, a question-answering benchmark for egocentric videos where the question-answering pairs are created from spatio-temporally grounded dynamic scene graphs capturing intricate relationships among actors, actions, and objects. We propose a systematic evaluation framework and evaluate several language-only and video large language models (video-LLMs) on this benchmark. We observe a performance gap in language-only and video-LLMs, especially on questions focusing on temporal ordering, thus identifying a research gap in the area of long-context video understanding. To promote the reproducibility of our findings and facilitate further research, the benchmark and accompanying code are available at the following GitHub page: https://github.com/fpv-iplab/EASG-bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EASG-Bench: Video Q&A Benchmark with Egocentric Action Scene Graphs Rodin, Ivan Wu, Tz-Ying Min, Kyle Sridhar, Sharath Nittur Furnari, Antonino Tripathi, Subarna Farinella, Giovanni Maria Computer Vision and Pattern Recognition We introduce EASG-Bench, a question-answering benchmark for egocentric videos where the question-answering pairs are created from spatio-temporally grounded dynamic scene graphs capturing intricate relationships among actors, actions, and objects. We propose a systematic evaluation framework and evaluate several language-only and video large language models (video-LLMs) on this benchmark. We observe a performance gap in language-only and video-LLMs, especially on questions focusing on temporal ordering, thus identifying a research gap in the area of long-context video understanding. To promote the reproducibility of our findings and facilitate further research, the benchmark and accompanying code are available at the following GitHub page: https://github.com/fpv-iplab/EASG-bench. |
| title | EASG-Bench: Video Q&A Benchmark with Egocentric Action Scene Graphs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05787 |