Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2107.12213v1
• Date: Mon, 26 Jul 2021 13:37:50 GMT
• Title: Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
• Authors: Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, Weiming Hu
• Abstract summary: We propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to learn different topologies. Our refinement method introduces few extra parameters and significantly reduces the difficulty of modeling channel-wise topologies. We develop a powerful graph convolutional network named CTR-GCN which notably outperforms state-of-the-art methods.
• Score: 40.103229224732196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. The proposed CTR-GC models channel-wise topologies through learning a shared topology as a generic prior for all channels and refining it with channel-specific correlations for each channel. Our refinement method introduces few extra parameters and significantly reduces the difficulty of modeling channel-wise topologies. Furthermore, via reformulating graph convolutions into a unified form, we find that CTR-GC relaxes strict constraints of graph convolutions, leading to stronger representation capability. Combining CTR-GC with temporal modeling modules, we develop a powerful graph convolutional network named CTR-GCN which notably outperforms state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.

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