  Graph convolutional networks (GCNs) have been widely used and achieved
remarkable results in skeleton-based action recognition. We think the key to
skeleton-based action recognition is a skeleton hanging in frames, so we focus
on how the Graph Convolutional Convolution networks learn different topologies
and effectively aggregate joint features in the global temporal and local
temporal. In this work, we propose three Channel-wise Tolopogy Graph
Convolution based on Channel-wise Topology Refinement Graph Convolution
(CTR-GCN). Combining CTR-GCN with two joint cross-attention modules can capture
the upper-lower body part and hand-foot relationship skeleton features. After
that, to capture features of human skeletons changing in frames we design the
Temporal Attention Transformers to extract skeletons effectively. The Temporal
Attention Transformers can learn the temporal features of human skeleton
sequences. Finally, we fuse the temporal features output scale with MLP and
classification. We develop a powerful graph convolutional network named Spatial
Temporal Effective Body-part Cross Attention Transformer which notably
high-performance on the NTU RGB+D, NTU RGB+D 120 datasets. Our code and models
are available at https://github.com/maclong01/STEP-CATFormer
