  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.
