A dual-branch video classifier uses depth and spatiotemporal features plus rank-weighted losses to categorize human-centric AI forgeries into spatial, appearance, and motion anomaly types on a new auto-labeled benchmark.
Multi-view clustering via deep concept factorization
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HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly
A dual-branch video classifier uses depth and spatiotemporal features plus rank-weighted losses to categorize human-centric AI forgeries into spatial, appearance, and motion anomaly types on a new auto-labeled benchmark.