Dynamic partitioning graph convolutional network for skeleton‐based action recognition

In skeleton‐based action recognition, most state‐of‐the‐art models are based on graph convolutional networks (GCNs), and propose various graph topologies to capture the inherent relationships between joints more effectively. However, existing GCN‐based methods are constrained by their reliance on independently proposed graph topologies, and often overlook the potential benefits of incorporating various graphs. To address this limitation, we propose a dynamic partitioning GCN (DPGCN) that can capture the dynamic dependencies of the skeletal structure and learn the relationships among subgraphs. The DPGCN, inspired by dynamic convolution, includes a dynamic partitioning graph convolution (DP‐GC) designed to extract features from subgraphs and predict dynamically adjusted weights. DP‐GC assigns predicted weights to multiple kernels and combines them, allowing the model to focus on the structural patterns. Our proposed DPGCN outperforms or achieves a performance comparable to state‐of‐the‐art methods on three benchmark datasets: NTU RGB+D, NTU RGB + D 120, and NW‐UCLA.