  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}.
