Action recognition via pose-based graph convolutional networks with intermediate dense supervision

Lei Shi, Yifan Zhang, Jian Cheng, Hanqing Lu

Abstract: Highlights•We propose a pose-based graph convolutional network (PGCN), which employs the graph convolutional module to model the spatiotemporal correlations among the pose-related features to produce a highly discriminative representation for human action recognition.•We point out the laziness problem of the backbone CNN, and further propose a novel intermediate dense supervision (IDS) to solve this problem. It is simple and effective, without the need for extra parameters and computations.•We evaluate our approach on three popular benchmarks for pose-based action recognition, where our approach achieves stateof-the-art performance on all of them.