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About this paper
Jiawei Tan, Hongxing Wang, Kang Dang, Jiaxin Li, Zhilong Ou
MASRC introduces a framework for video scene detection that explicitly models the distinct temporal dynamics of visual entities and places using modality-aware graph networks. The system achieves state-of-the-art performance on the MovieNet dataset and demonstrates strong generalization across other benchmarks by comparing multi-shot contexts for boundary detection.
Key insights
- Visual modalities exhibit distinct temporal dynamics (long-term for entities, short-term for places) that are best modeled separately rather than with a unified approach.
- Explicitly comparing the semantic contexts on both sides of a potential scene boundary is crucial for accurate scene transition detection, outperforming methods relying on single-shot predictions.
- Graph Neural Networks are effective for capturing both long-range affinities between intermittently appearing entities and short-range continuities within places across video shots.
Summary by alphaXiv.
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