In this work, we consider the problem of cross-domain 3D action recognition in the open-set setting, which has been rarely explored before. Specifically, there is a source domain and a target domain that contain the skeleton sequences with different styles and categories, and our purpose is to cluster the target data by utilizing the labeled source data and unlabeled target data. For such a challenging task, this paper presents a novel approach dubbed CoDT to collaboratively cluster the domain-shared features and target-specific features. CoDT consists of two parallel branches. One branch aims to learn domain-shared features with supervised learning in the source domain, while the other is to learn target-specific features using contrastive learning in the target domain. To cluster the features, we propose an online clustering algorithm that enables simultaneous promotion of robust pseudo label generation and feature clustering. Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches. We conduct extensive experiments on multiple cross-domain 3D action recognition datasets, and the results demonstrate the effectiveness of our method.

翻译：在这项工作中,我们考虑了在开放设置环境中跨域 3D 行动识别的问题,这个问题以前很少探讨过。具体地说,有一个源域和目标域,包含不同风格和类别的骨架序列,我们的目的是通过使用标签源数据和未标签目标数据对目标数据进行分组。对于这种具有挑战性的任务,本文件提出了一个新颖的办法,称为CODT, 将共享域特征和目标特定特征协同集中在一起。 CoDT由两个平行分支组成。一个分支的目的是学习在源域有监督学习的共享域特征,而另一个分支则是在目标域利用对比学习学习特定目标特征。为了对目标域进行分组,我们建议了在线群集算法,以便能够同时促进强大的伪标签生成和特征组合。此外,为了利用共享域特征和目标特定特征的互补性,我们提出了一个新的协作集群战略,以加强两个分支之间的对称关系的一致性。我们进行了广泛的实验,涉及多个跨部3D行动识别数据集,并展示了我们的方法的有效性。