Multimodal High-order Relation Transformer for Scene Boundary Detection

Scene boundary detection breaks down long videos into meaningful story-telling units and plays a crucial role in high-level video understanding. Despite significant advancements in this area, this task remains a challenging problem as it requires a comprehensive understanding of multimodal cues and high-level semantics. To tackle this issue, we propose a multimodal high-order relation transformer, which integrates a high-order encoder and an adaptive decoder in a unified framework. By modeling the multimodal cues and exploring similarities between the shots, the encoder is capable of capturing high-order relations between shots and extracting shot features with context semantics. By clustering the shots adaptively, the decoder can discover more universal switch pattern between successive scenes, thus helping scene boundary detection. Extensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art video scene detection methods.