Graph Contrastive Learning for Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2301.10900v2
• Date: Sat, 10 Jun 2023 10:32:06 GMT
• Title: Graph Contrastive Learning for Skeleton-based Action Recognition
• Authors: Xiaohu Huang, Hao Zhou, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang, Xinggang Wang, Wenyu Liu, Bin Feng
• Abstract summary: We propose a graph contrastive learning framework for skeleton-based action recognition. SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative. SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current graph convolutional networks.
• Score: 85.86820157810213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature aggregation. However, we argue that such context is still \textit{local} since the rich cross-sequence relations have not been explicitly investigated. In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences. In specific, SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative, \emph{i.e.,} intra-class compact and inter-class dispersed, which improves the GCN capacity to distinguish various action patterns. Besides, two memory banks are designed to enrich cross-sequence context from two complementary levels, \emph{i.e.,} instance and semantic levels, enabling graph contrastive learning in multiple context scales. Consequently, SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current GCNs. Without loss of generality, we combine SkeletonGCL with three GCNs (2S-ACGN, CTR-GCN, and InfoGCN), and achieve consistent improvements on NTU60, NTU120, and NW-UCLA benchmarks. The source code will be available at \url{https://github.com/OliverHxh/SkeletonGCL}.

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