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.

翻译：在基于骨架的人体活动理解中，现有方法通常采用对比学习范式来构建一个具有判别性的特征空间。然而，这些方法大多未能利用类别间的结构相似性，并且忽略了异常正样本的影响。在本研究中，我们提出了ACLNet，一种亲和性对比学习网络，旨在探索人体活动类别之间复杂的聚类关系，以提升特征判别力。具体而言，我们提出了一种亲和性度量来优化相似性计算，从而形成活动超类，这些超类能提供更具信息量的对比信号。我们还引入了一种动态温度调度机制，以自适应地调整针对不同超类的惩罚强度。此外，我们采用了一种基于间隔的对比策略，以改善类内困难正样本与负样本的分离效果。在NTU RGB+D 60、NTU RGB+D 120、Kinetics-Skeleton、PKU-MMD、FineGYM和CASIA-B数据集上进行的大量实验表明，我们的方法在基于骨架的动作识别、步态识别和行人重识别任务上具有优越性。源代码可在 https://github.com/firework8/ACLNet 获取。