Graph convolutional networks (GCNs) achieve promising performance for skeleton-based action recognition. However, in most GCN-based methods, the spatial-temporal graph convolution is strictly restricted by the graph topology while only captures the short-term temporal context, thus lacking the flexibility of feature extraction. In this work, we present a novel architecture, named Graph Convolutional skeleton Transformer (GCsT), which addresses limitations in GCNs by introducing Transformer. Our GCsT employs all the benefits of Transformer (i.e. dynamical attention and global context) while keeps the advantages of GCNs (i.e. hierarchy and local topology structure). In GCsT, the spatial-temporal GCN forces the capture of local dependencies while Transformer dynamically extracts global spatial-temporal relationships. Furthermore, the proposed GCsT shows stronger expressive capability by adding additional information present in skeleton sequences. Incorporating the Transformer allows that information to be introduced into the model almost effortlessly. We validate the proposed GCsT by conducting extensive experiments, which achieves the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA datasets.

本文提出了一种名为Hierarchical Graph Convolutional skeleton Transformer (HGCT)的新型架构，通过引入Disentangled Spatiotemporal Transformer块，利用全局时空注意力和本地信息增强相互协同，以解决基于骨架的动作识别中的邻域限制和空间时间特征表示相互干扰等问题，同时具有计算效率高、解释性好等优点。

用于动作识别的分层图卷积骨架变换器