High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition

Recently, significant achievements have been made in skeleton-based human action recognition with the emergence of graph convolutional networks (GCNs). However, the state-of-the-art (SOTA) models used for this task focus on constructing more complex higher-order connections between joint nodes to describe skeleton information, which leads to complex inference processes and high computational costs, resulting in reduced model's practicality. To address the slow inference speed caused by overly complex model structures, we introduce re-parameterization and over-parameterization techniques to GCNs, and propose two novel high-performance inference graph convolutional networks, namely HPI-GCN-RP and HPI-GCN-OP. HPI-GCN-RP uses re-parameterization technique to GCNs to achieve a higher inference speed with competitive model performance. HPI-GCN-OP further utilizes over-parameterization technique to bring significant performance improvement with inference speed slightly decreased. Experimental results on the two skeleton-based action recognition datasets demonstrate the effectiveness of our approach. Our HPI-GCN-OP achieves an accuracy of 93% on the cross-subject split of the NTU-RGB+D 60 dataset, and 90.1% on the cross-subject benchmark of the NTU-RGB+D 120 dataset and is 4.5 times faster than HD-GCN at the same accuracy.