用关系建模提升视频镜头边界检测精度，支持精准解析转场类型。
OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer
- 基于镜头查询的密集视频Transformer，联合预测镜头区间与内部/跨镜头关系。
- 在合成数据上训练，实现精确边界标注，避免人工标注噪声。
- 新基准测试覆盖广泛场景，适合需要高精度镜头分析的研究者。
镜头边界检测（SBD）旨在自动识别镜头变化并划分视频为连贯镜头。尽管已有广泛研究，现有方法常产生不可解释的边界、遗漏细微但有害的不连续性，且依赖噪声大、多样性差的标注和过时基准。为此，我们提出OmniShotCut，将SBD建模为结构化关系预测任务，通过镜头查询驱动的密集视频Transformer，联合估计镜头范围及其内部与跨镜头关系。为避免不精确的人工标注，采用全自动合成转场流水线，可精确生成主要转场类型及其参数化变体。同时引入OmniShotCutBench，一个现代多领域基准，支持全面诊断评估。在多个基准上的实验验证了方法的有效性与泛化能力。
原文摘要 · Abstract (English)
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boundaries on transitions, miss subtle yet harmful discontinuities, and rely on noisy, low-diversity annotations and outdated benchmarks.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.
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