G3CN: Gaussian Topology Refinement Gated Graph Convolutional Network for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2509.07335v1
• Date: Tue, 09 Sep 2025 02:19:24 GMT
• Title: G3CN: Gaussian Topology Refinement Gated Graph Convolutional Network for Skeleton-Based Action Recognition
• Authors: Haiqing Ren, Zhongkai Luo, Heng Fan, Xiaohui Yuan, Guanchen Wang, Libo Zhang,
• Abstract summary: Graph Convolutional Networks (GCNs) have proven to be highly effective for skeleton-based action recognition.<n>We propose a novel approach, Gaussian Topology Refinement Gated Graph Convolution (G$3$CN), to address the challenge of distinguishing ambiguous actions in skeleton-based action recognition.
• Score: 16.348926876428038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Convolutional Networks (GCNs) have proven to be highly effective for skeleton-based action recognition, primarily due to their ability to leverage graph topology for feature aggregation, a key factor in extracting meaningful representations. However, despite their success, GCNs often struggle to effectively distinguish between ambiguous actions, revealing limitations in the representation of learned topological and spatial features. To address this challenge, we propose a novel approach, Gaussian Topology Refinement Gated Graph Convolution (G$^{3}$CN), to address the challenge of distinguishing ambiguous actions in skeleton-based action recognition. G$^{3}$CN incorporates a Gaussian filter to refine the skeleton topology graph, improving the representation of ambiguous actions. Additionally, Gated Recurrent Units (GRUs) are integrated into the GCN framework to enhance information propagation between skeleton points. Our method shows strong generalization across various GCN backbones. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and NW-UCLA benchmarks demonstrate that G$^{3}$CN effectively improves action recognition, particularly for ambiguous samples.

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