Multi-scale Graph Convolution with Corrective Contrastive Learning for Skeleton-based Action Recognition

Abstract

For pursuing accurate skeleton-based action recognition, many existing graph-based approaches deploy the higher-order polynomials of the skeletal adjacency matrix to model the node correlations of distant neighbours. To further capture robust graphical patterns, a novel multi-scale graph convolution operator is proposed, which enables to aggregate multi-scale dependencies and capture long-range joint relationships on human skeleton graph. Additionally, a novel corrective contrastive learning strategy is proposed, which aims to distinguish the representative clues and calibrate the confused action clips in the feature space. Comprehensive experiments validate the effectiveness and superiority of our proposed method over state-of-the-art approaches on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.