Geometry-Constrained Dynamic Hypergraph Convolutional Network with Contrastive Score Refinement for Skeleton-Based Action Recognition

The paper introduces DyGCN, a Geometry-Constrained Dynamic Hypergraph Convolutional Network designed for skeleton-based action recognition. It features a Dynamic Hypergraph Construction Network with Contrastive Score Refinement (DyGCT) that includes a GeoHC module for action-specific hyperedge construction and a CSR module that refines pairwise compatibility scores via prototype-based contrastive learning during training. Experiments on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets demonstrate state‑of‑the‑art performance, and the source code is publicly available.

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• computer-vision
• benchmarks