Self-Supervised Representation Learning for Skeleton-Based Group Activity Recognition

Group activity recognition (GAR) is a challenging task for discerning the behavior of a group of actors. This paper aims at learning discriminative representation for GAR in a self-supervised manner based on human skeletons. As modeling relations between actors lie at the center of GAR, we propose a valid self-supervised learning pretext task with a matching framework, where a representation model is driven to identify subgroups in a synthetic group based on actors' skeleton sequences. For backbone networks, while spatial-temporal graph convolution networks have dominated the skeleton-based action recognition, they under-explore the group relevant interactions among actors. To address this issue, we come up with a novel plug-in Actor-Association Graph Convolution Module (AAGCM) based on inductive graph convolution, which can be integrated into many common backbones. It can not only model the interactions at different levels but also adapt to variable group sizes. The effectiveness of our approaches is demonstrated by extensive experiments on three benchmark datasets: Volleyball, Collective Activity, and Mutual NTU.