Multi-temporal scale aggregation refinement graph convolutional network for skeleton-based action recognition

Skeleton-based human action recognition is gaining significant attention and finding widespread application in various fields, such as virtual reality and human-computer interaction systems. Recent studies have highlighted the effectiveness of graph convolutional network (GCN) based methods in this task, leading to a remarkable improvement in prediction accuracy. However, most GCN-based methods overlook the varying contributions of self, centripetal and centrifugal subsets. Besides, only a single-scale temporal feature is adopted, and the multi-temporal scale information is ignored. To this end, firstly, in order to differentiate the importance of different skeleton subsets, we develop a refinement graph convolution, which can adaptively learn a weight for each subset feature. Secondly, a multi-temporal scale aggregation module is proposed to extract more discriminative temporal dynamic information. Furthermore, a multi-temporal scale aggregation refinement graph convolutional network (MTSA-RGCN) is proposed, and four-stream structure is also adopted in this paper, which can comprehensively model complementary features and eventually achieves a significant performance boost. In the empirical experiments, the performance of our approach has been greatly improved on both NTU-RGB+D 60 and NTU-RGB+D 120 datasets, compared to other state-of-the-art methods.