Skeleton motion topology-masked prediction and contrastive learning for self-supervised human action recognition
Year: 2026
Type: article
Abstract: To address the limitations in data augmentation and neglect of joint dependencies in self-supervised human action recognition, this paper proposes a hybrid framework that integrates topology-masked motion modeling with contrastive learning. The proposed motion topology-masking technique jointly encodes skeletal topology and motion dynamics, preventing the model from over-focusing on temporally salient regions of prominent motions. We employ a multi-stage hybrid augmentation strategy, combining conventional and extreme augmentation methods to generate diverse, enriched positive pairs for contrastive learning. Additionally, we introduce a trajectory-guided feature dropping module, which selectively discards critical features based on trajectory attention maps, preventing the model from avoiding excessive focus on local joint trajectories. This approach effectively leverages large-scale unlabeled skeleton data through self-supervised learning, significantly reducing reliance on costly annotated datasets. Extensive experiments on NTU-60, NTU-120, and PKU-MMD demonstrate that the proposed model achieves superior performance in both occluded scenarios under complex environments and low-supervision conditions. It effectively mitigates visual interference and annotation scarcity while substantially improving action recognition accuracy.
Source: Scientific Reports
Authors: Yan Hui, Fengyu Li, Xiuhua Hu, Xin Guo, Chao Shen
Institution: Xi'an Technological University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G7494271127, G8074822621
OpenAlex ID: W7128472194