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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>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 https:\/\/github.com\/shanice-l\/st-cubism.<\/jats:p>","DOI":"10.1145\/3472722","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T12:56:32Z","timestamp":1645016192000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition"],"prefix":"10.1145","volume":"18","author":[{"given":"Yansong","family":"Tang","sequence":"first","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xumin","family":"Yu","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwen","family":"Lu","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2929257"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00642"},{"key":"e_1_3_2_6_2","first-page":"1597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. 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