Rapid progress and superior performance have been achieved for skeleton-based
action recognition recently. In this article, we investigate this problem under
a cross-dataset setting, which is a new, pragmatic, and challenging task in
real-world scenarios. Following the unsupervised domain adaptation (UDA)
paradigm, the action labels are only available on a source dataset, but
unavailable on a target dataset in the training stage. Different from the
conventional adversarial learning-based approaches for UDA, we utilize a
self-supervision scheme to reduce the domain shift between two skeleton-based
action datasets. Our inspiration is drawn from Cubism, an art genre from the
early 20th century, which breaks and reassembles the objects to convey a
greater context. By segmenting and permuting temporal segments or human body
parts, we design two self-supervised learning classification tasks to explore
the temporal and spatial dependency of a skeleton-based action and improve the
generalization ability of the model. We conduct experiments on six datasets for
skeleton-based action recognition, including three large-scale datasets (NTU
RGB+D, PKU-MMD, and Kinetics) where new cross-dataset settings and benchmarks
are established. Extensive results demonstrate that our method outperforms
state-of-the-art approaches. The source codes of our model and all the compared
methods are available at this https URL

本篇文章研究骨架动作识别的跨数据集问题，并提出了一种基于自监督的域自适应方法，通过对时间和空间分段来探索模型的时空依赖关系并提高模型的泛化能力，实验结果证明该方法优于其他方法。