← 研踪 PaperTrailarXiv:2611.02685 · v1
Geometry-Constrained Dynamic Hypergraph Convolutional Network with Contrastive Score Refinement for Skeleton-Based Action Recognition
首次提交 2026-11-02 15:37
版本更新 2026-11-02 15:37
雷达观测 2026-11-04 23:42
摘要
Skeleton-based action recognition is a pivotal task in computer vision; however, conventional Graph Convolutional Networks (GCNs) are often constrained by pairwise joint modeling, which fails to capture the intricate high-order interactions inherent in human movement. While recent hypergraph-based methods attempt to address this limitation, they typically rely on static structures derived from anatomical priors, lacking the flexibility to capture dynamic, action-specific joint correlations. To this end, we propose DyGCN, a Geometry-Constrained Dynamic Hypergraph Convolutional Network for skeleton-based action recognition. At the core of DyGCN is the Dynamic Hypergraph Construction Network with Contrastive Score Refinement (DyGCT) block, which comprises two novel components: the Geometry-Constrained Dynamic Hypergraph Construction (GeoHC) module and the Contrastive Score Refinement (CSR) module. Specifically, GeoHC dynamically constructs action-specific hyperedges by leveraging geometry-constrained second-order compatibility, transcending the limitations of static hyperedge structures. Furthermore, CSR explicitly refines the pairwise compatibility scores during hypergraph construction through prototype-based contrastive learning, fostering more discriminative action-specific hyperedges, with zero inference-time overhead as CSR operates only during training. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA validate the effectiveness of DyGCN, achieving state-of-the-art performance on widely used benchmarks. The source code is available at https://github.com/marvineta0405/DyGCN.
作者备注：19 pages, 4 figures, BMVC 2026
观测到的版本
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v1 · 2026-11-02 15:37 · 最新观测版本
Geometry-Constrained Dynamic Hypergraph Convolutional Network with Contrastive Score Refinement for Skeleton-Based Action Recognition
Skeleton-based action recognition is a pivotal task in computer vision; however, conventional Graph Convolutional Networks (GCNs) are often constrained by pairwise joint modeling, which fails to capture the intricate high-order interactions inherent in human movement. While recent hypergraph-based methods attempt to address this limitation, they typically rely on static structures derived from anatomical priors, lacking the flexibility to capture dynamic, action-specific joint correlations. To this end, we propose DyGCN, a Geometry-Constrained Dynamic Hypergraph Convolutional Network for skeleton-based action recognition. At the core of DyGCN is the Dynamic Hypergraph Construction Network with Contrastive Score Refinement (DyGCT) block, which comprises two novel components: the Geometry-Constrained Dynamic Hypergraph Construction (GeoHC) module and the Contrastive Score Refinement (CSR) module. Specifically, GeoHC dynamically constructs action-specific hyperedges by leveraging geometry-constrained second-order compatibility, transcending the limitations of static hyperedge structures. Furthermore, CSR explicitly refines the pairwise compatibility scores during hypergraph construction through prototype-based contrastive learning, fostering more discriminative action-specific hyperedges, with zero inference-time overhead as CSR operates only during training. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA validate the effectiveness of DyGCN, achieving state-of-the-art performance on widely used benchmarks. The source code is available at https://github.com/marvineta0405/DyGCN.
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