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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2312.10300 |
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| _version_ | 1866917912832376832 |
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| author | Han, Mingfei Yang, Linjie Chang, Xiaojun Yao, Lina Wang, Heng |
| author_facet | Han, Mingfei Yang, Linjie Chang, Xiaojun Yao, Lina Wang, Heng |
| contents | A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. Nevertheless, the generated imperfect summaries can already achieve competitive performance on existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_10300 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos Han, Mingfei Yang, Linjie Chang, Xiaojun Yao, Lina Wang, Heng Computer Vision and Pattern Recognition A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. Nevertheless, the generated imperfect summaries can already achieve competitive performance on existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries. |
| title | Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.10300 |