Affinity Contrastive Learning for Skeleton-based Human Activity Understanding

• URL: http://arxiv.org/abs/2601.16694v1
• Date: Fri, 23 Jan 2026 12:20:36 GMT
• Title: Affinity Contrastive Learning for Skeleton-based Human Activity Understanding
• Authors: Hongda Liu, Yunfan Liu, Min Ren, Lin Sui, Yunlong Wang, Zhenan Sun,
• Abstract summary: ACLNet is an Affinity Contrastive Learning Network that explores the intricate clustering relationships among human activity classes.<n>We propose an affinity metric to refine similarity measurements, thereby forming activity superclasses that provide more informative contrastive signals.<n>In addition, we employ a margin-based contrastive strategy to improve the separation of hard positive and negative samples within classes.
• Score: 36.788675803693486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In skeleton-based human activity understanding, existing methods often adopt the contrastive learning paradigm to construct a discriminative feature space. However, many of these approaches fail to exploit the structural inter-class similarities and overlook the impact of anomalous positive samples. In this study, we introduce ACLNet, an Affinity Contrastive Learning Network that explores the intricate clustering relationships among human activity classes to improve feature discrimination. Specifically, we propose an affinity metric to refine similarity measurements, thereby forming activity superclasses that provide more informative contrastive signals. A dynamic temperature schedule is also introduced to adaptively adjust the penalty strength for various superclasses. In addition, we employ a margin-based contrastive strategy to improve the separation of hard positive and negative samples within classes. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120, Kinetics-Skeleton, PKU-MMD, FineGYM, and CASIA-B demonstrate the superiority of our method in skeleton-based action recognition, gait recognition, and person re-identification. The source code is available at https://github.com/firework8/ACLNet.

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