ATOM: Self-supervised human action recognition using atomic motion representation learning

Abstract

Self-supervised learning (SSL) is a promising method for gaining perception and common sense from unlabelled data. Existing approaches to analyzing human body skeletons address the problem similar to SSL models for image and video understanding, but pixel data is far more challenging than coordinates. This paper presents ATOM, an SSL model designed for skeleton-based data analysis. Unlike video-based SSL approaches, ATOM leverages atomic movements within skeleton actions to achieve a more fine-grained representation. The proposed architecture predicts the action order at the frame level, leading to improved perceptions and representations of each action. ATOM outperforms state-of-the-art approaches in two well-known datasets (NTU RGB + D and NTU-120 RGB + D), and its weight transferability enables performance improvements on supervised and semi-supervised tasks, up to 4.4% (3.3% p.p.) and 14.1% (6.3% p.p.), respectively, in Top-1 Accuracy.