OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918516054032384 |
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| author | Wang, Boyang Xu, Guangyi Zhang, Jiahui Tang, Zhipeng Cheng, Zezhou |
| author_facet | Wang, Boyang Xu, Guangyi Zhang, Jiahui Tang, Zhipeng Cheng, Zezhou |
| contents | Shot Boundary Detection (SBD) aims to automatically identify shot changes and divide a video into coherent shots. While SBD was widely studied in the literature, existing methods often produce non-interpretable boundaries on transitions, miss subtle yet harmful discontinuities, and rely on noisy, low-diversity annotations and outdated benchmarks. To alleviate these limitations, we propose OmniShotCut to formulate SBD as structured relational prediction, jointly estimating shot ranges with intra-shot relations and inter-shot relations, by a shot query-based dense video Transformer. To avoid imprecise manual labeling, we adopt a fully synthetic transition synthesis pipeline that automatically reproduces major transition families with precise boundaries and parameterized variants. We also introduce OmniShotCutBench, a modern wide-domain benchmark enabling holistic and diagnostic evaluation. Experiments on the benchmarks demonstrate the effectiveness and generality of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24762 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer Wang, Boyang Xu, Guangyi Zhang, Jiahui Tang, Zhipeng Cheng, Zezhou Computer Vision and Pattern Recognition Shot Boundary Detection (SBD) aims to automatically identify shot changes and divide a video into coherent shots. While SBD was widely studied in the literature, existing methods often produce non-interpretable boundaries on transitions, miss subtle yet harmful discontinuities, and rely on noisy, low-diversity annotations and outdated benchmarks. To alleviate these limitations, we propose OmniShotCut to formulate SBD as structured relational prediction, jointly estimating shot ranges with intra-shot relations and inter-shot relations, by a shot query-based dense video Transformer. To avoid imprecise manual labeling, we adopt a fully synthetic transition synthesis pipeline that automatically reproduces major transition families with precise boundaries and parameterized variants. We also introduce OmniShotCutBench, a modern wide-domain benchmark enabling holistic and diagnostic evaluation. Experiments on the benchmarks demonstrate the effectiveness and generality of our method. |
| title | OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.24762 |