{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:45:53Z","timestamp":1792759553345,"version":"4.5.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s13042-026-03263-6","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:39:52Z","timestamp":1786700392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-supervised skeleton action recognition based on graph prototype learning"],"prefix":"10.1007","volume":"17","author":[{"given":"Zhijie","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"3263_CR1","doi-asserted-by":"crossref","unstructured":"Hatamimajoumerd E, Kakhaki PD, Huang X, Luan L, Amraee S, Ostadabbas S (2024) Challenges in video-based infant action recognition: a critical examination of the state of the art. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp 21\u201330","DOI":"10.1109\/WACVW60836.2024.00010"},{"issue":"6","key":"3263_CR2","doi-asserted-by":"publisher","first-page":"7301","DOI":"10.1109\/TMC.2023.3333043","volume":"23","author":"Z Wang","year":"2023","unstructured":"Wang Z, Chen Y, Zheng H, Liu M, Huang P (2023) Body rfid skeleton-based human activity recognition using graph convolution neural network. IEEE Trans Mob Comput 23(6):7301\u20137317","journal-title":"IEEE Trans Mob Comput"},{"key":"3263_CR3","doi-asserted-by":"publisher","first-page":"27394","DOI":"10.52202\/075280-1193","volume":"36","author":"Q Zhao","year":"2023","unstructured":"Zhao Q, Zheng C, Liu M, Chen C (2023) A single 2d pose with context is worth hundreds for 3d human pose estimation. Adv Neural Inf Process Syst 36:27394\u201327413","journal-title":"Adv Neural Inf Process Syst"},{"key":"3263_CR4","doi-asserted-by":"publisher","first-page":"3507","DOI":"10.1109\/TIP.2023.3286254","volume":"32","author":"Z Tu","year":"2023","unstructured":"Tu Z, Liu Y, Zhang Y, Mu Q, Yuan J (2023) Dtcm: joint optimization of dark enhancement and action recognition in videos. IEEE Trans Image Process 32:3507\u20133520","journal-title":"IEEE Trans Image Process"},{"key":"3263_CR5","first-page":"1701","volume":"37","author":"M Liu","year":"2023","unstructured":"Liu M, Meng F, Chen C, Wu S (2023) Novel motion patterns matter for practical skeleton-based action recognition. Proc AAAI Conf Artif Intell 37:1701\u20131709","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR6","unstructured":"Zhang J, Lin L, Yang S, Liu J (2024) Self-supervised skeleton-based action representation learning: a benchmark and beyond. arXiv preprint arXiv:2406.02978"},{"issue":"23","key":"3263_CR7","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.3390\/electronics13234733","volume":"13","author":"L Zhao","year":"2024","unstructured":"Zhao L, Lin Z, Sun R, Wang A (2024) A review of state-of-the-art methodologies and applications in action recognition. Electronics 13(23):4733","journal-title":"Electronics"},{"key":"3263_CR8","first-page":"6225","volume":"38","author":"J Xie","year":"2024","unstructured":"Xie J, Meng Y, Zhao Y, Nguyen A, Yang X, Zheng Y (2024) Dynamic semantic-based spatial graph convolution network for skeleton-based human action recognition. Proc AAAI Conf Artif Intel 38:6225\u20136233","journal-title":"Proc AAAI Conf Artif Intel"},{"key":"3263_CR9","doi-asserted-by":"crossref","unstructured":"Liu Y, Xu Q, Wen P, Dai S, Huang Q (2025) When the future becomes the past: taming temporal correspondence for self-supervised video representation learning. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp. 24033\u201324044","DOI":"10.1109\/CVPR52734.2025.02238"},{"key":"3263_CR10","first-page":"525","volume":"37","author":"J Dong","year":"2023","unstructured":"Dong J, Sun S, Liu Z, Chen S, Liu B, Wang X (2023) Hierarchical contrast for unsupervised skeleton-based action representation learning. Proc AAAI Conf Artif Intell 37:525\u2013533","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR11","doi-asserted-by":"crossref","unstructured":"Lin L, Song S, Yang W, Liu J (2020) Ms2l: Multi-task self-supervised learning for skeleton based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia. pp 2490\u20132498","DOI":"10.1145\/3394171.3413548"},{"key":"3263_CR12","doi-asserted-by":"crossref","unstructured":"Cormier M, Schmid Y, Beyerer J (2024) Enhancing skeleton-based action recognition in real-world scenarios through realistic data augmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp 290\u2013299","DOI":"10.1109\/WACVW60836.2024.00038"},{"issue":"4","key":"3263_CR13","doi-asserted-by":"publisher","first-page":"747","DOI":"10.3390\/electronics13040747","volume":"13","author":"C Xin","year":"2024","unstructured":"Xin C, Kim S, Cho Y, Park KS (2024) Enhancing human action recognition with 3d skeleton data: a comprehensive study of deep learning and data augmentation. Electronics 13(4):747","journal-title":"Electronics"},{"key":"3263_CR14","first-page":"5949","volume":"38","author":"C Wu","year":"2024","unstructured":"Wu C, Wu X-J, Kittler J, Xu T, Ahmed S, Awais M, Feng Z (2024) Scd-net: spatiotemporal clues disentanglement network for self-supervised skeleton-based action recognition. Proc AAAI Conf Artif Intell 38:5949\u20135957","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR15","first-page":"3825","volume":"37","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Duan H, Rao A, Su B, Wang J (2023) Self-supervised action representation learning from partial spatio-temporal skeleton sequences. Proc AAAI Conf Artif Intell 37:3825\u20133833","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR16","doi-asserted-by":"crossref","unstructured":"Liu H, Liu Y, Ren M, Wang H, Wang Y, Sun Z (2025) Revealing key details to see differences: a novel prototypical perspective for skeleton-based action recognition. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp 29248\u201329257","DOI":"10.1109\/CVPR52734.2025.02723"},{"issue":"11","key":"3263_CR17","doi-asserted-by":"publisher","first-page":"10578","DOI":"10.1109\/TCSVT.2024.3410301","volume":"34","author":"J Hu","year":"2024","unstructured":"Hu J, Hou Y, Guo Z, Gao J (2024) Global and local contrastive learning for self-supervised skeleton-based action recognition. IEEE Trans Circuits Syst Video Technol 34(11):10578\u201310589","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"3263_CR18","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13640-025-00677-0","volume":"2025","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Wang Y (2025) A comprehensive survey on rgb-d-based human action recognition: algorithms, datasets, and popular applications. EURASIP J Image Video Process 2025(1):15","journal-title":"EURASIP J Image Video Process"},{"key":"3263_CR19","doi-asserted-by":"crossref","unstructured":"Peng Z, Wang Z, Sun M, Lv Z, Wang Y, Li P, An F (2025) Graph convolutional networks for 3d skeleton-based scoliosis screening using gait sequences: peng Z. et al. The Visual Computer, pp 1\u201313","DOI":"10.1007\/s00371-025-03983-w"},{"key":"3263_CR20","doi-asserted-by":"crossref","unstructured":"Zhou Y, Yan X, Cheng Z-Q, Yan Y, Dai Q, Hua X-S (2024) Blockgcn: redefine topology awareness for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2049\u20132058","DOI":"10.1109\/CVPR52733.2024.00200"},{"key":"3263_CR21","doi-asserted-by":"crossref","unstructured":"Le KT, Pham HH, Bui NA, Lie WN, Bui ND (2024) A review on skeleton-based early action recognition. In: The International Conference on Intelligent Systems & Networks, Springer, pp 355\u2013364","DOI":"10.1007\/978-981-97-5504-2_42"},{"key":"3263_CR22","doi-asserted-by":"crossref","unstructured":"Lee J, Lee M, Lee D, Lee S (2023) Hierarchically decomposed graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10444\u201310453","DOI":"10.1109\/ICCV51070.2023.00958"},{"key":"3263_CR23","doi-asserted-by":"publisher","first-page":"109486","DOI":"10.1016\/j.sigpro.2024.109486","volume":"221","author":"C Liu","year":"2024","unstructured":"Liu C, Jiang Y, Du C, Li Z (2024) Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation. Signal Process 221:109486","journal-title":"Signal Process"},{"key":"3263_CR24","doi-asserted-by":"publisher","first-page":"1918","DOI":"10.1109\/LSP.2022.3204190","volume":"29","author":"C Liu","year":"2022","unstructured":"Liu C, Zhao X, Li Z, Yan Z, Du C (2022) A novel two-stage knowledge distillation framework for skeleton-based action prediction. IEEE Signal Process Lett 29:1918\u20131922","journal-title":"IEEE Signal Process Lett"},{"key":"3263_CR25","doi-asserted-by":"crossref","unstructured":"Lee M, Cho S, Lee S, Park C, Lee S (2023) Unsupervised video object segmentation via prototype memory network. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 5924\u20135934","DOI":"10.1109\/WACV56688.2023.00587"},{"key":"3263_CR26","doi-asserted-by":"crossref","unstructured":"Wei Y, Ye J, Huang Z, Zhang J, Shan H (2023) Online prototype learning for online continual learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 18764\u201318774","DOI":"10.1109\/ICCV51070.2023.01720"},{"key":"3263_CR27","doi-asserted-by":"publisher","first-page":"111896","DOI":"10.1016\/j.patcog.2025.111896","volume":"169","author":"G Shi","year":"2026","unstructured":"Shi G, Liang X, Lin X (2026) Rethinking multi-pattern mining from a perspective of pattern prototype learning. Pattern Recogn 169:111896","journal-title":"Pattern Recogn"},{"key":"3263_CR28","doi-asserted-by":"crossref","unstructured":"Cheng P, Lin L, Lyu J, Huang Y, Luo W, Tang X (2023) Prior: prototype representation joint learning from medical images and reports. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 21361\u201321371","DOI":"10.1109\/ICCV51070.2023.01953"},{"key":"3263_CR29","first-page":"3145","volume":"38","author":"L Li","year":"2024","unstructured":"Li L, Ji W, Wu Y, Li M, Qin Y, Wei L, Zimmermann R (2024) Panoptic scene graph generation with semantics-prototype learning. Proc AAAI Conf Artif Intell 38:3145\u20133153","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR30","doi-asserted-by":"crossref","unstructured":"Liu C, Liu S, Qiu H, Li Z (2025) Adaptive part-level embedding gcn: towards robust skeleton-based one-shot action recognition. IEEE Trans Instrum Meas","DOI":"10.1109\/TIM.2025.3556824"},{"key":"3263_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.130010","author":"BH Ngo","year":"2025","unstructured":"Ngo BH, Choi TJ, Cho SI (2025) Towards enhancing prototypes driven by graph convolutional networkfor domain adaptation. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2025.130010","journal-title":"Expert Syst Appl"},{"key":"3263_CR32","first-page":"6191","volume":"39","author":"BH Ngo","year":"2025","unstructured":"Ngo BH, Bui DC, Do-Tran N-T, Choi TJ (2025) Higda: hierarchical graph of nodes to learn local-to-global topology for semi-supervised domain adaptation. Proc AAAI Conf Artif Intell 39:6191\u20136199","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"5","key":"3263_CR33","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1007\/s10462-021-10107-y","volume":"55","author":"L Feng","year":"2022","unstructured":"Feng L, Zhao Y, Zhao W, Tang J (2022) A comparative review of graph convolutional networks for human skeleton-based action recognition. Artif Intell Rev 55(5):4275\u20134305","journal-title":"Artif Intell Rev"},{"key":"3263_CR34","first-page":"844","volume":"37","author":"L Hu","year":"2023","unstructured":"Hu L, Liu DD, Zhang Q, Naseem U, Lai ZY (2023) Self-supervised learning for multilevel skeleton-based forgery detection via temporal-causal consistency of actions. Proc AAAI Conf Artif Intell 37:844\u2013853","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR35","doi-asserted-by":"publisher","first-page":"107210","DOI":"10.1016\/j.engappai.2023.107210","volume":"127","author":"Y Xia","year":"2024","unstructured":"Xia Y, Gao Q, Wu W, Cao Y (2024) Skeleton-based action recognition based on multidimensional adaptive dynamic temporal graph convolutional network. Eng Appl Artif Intell 127:107210","journal-title":"Eng Appl Artif Intell"},{"issue":"6","key":"3263_CR36","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.1109\/TCSI.2023.3254610","volume":"70","author":"W Wu","year":"2023","unstructured":"Wu W, Tu F, Niu M, Yue Z, Liu L, Wei S, Li X, Hu Y, Yin S (2023) Star: an stgcn architecture for skeleton-based human action recognition. IEEE Trans Circuits Syst I Regul Pap 70(6):2370\u20132383","journal-title":"IEEE Trans Circuits Syst I Regul Pap"},{"key":"3263_CR37","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.neucom.2023.03.070","volume":"537","author":"Q Men","year":"2023","unstructured":"Men Q, Ho ES, Shum HP, Leung H (2023) Focalized contrastive view-invariant learning for self-supervised skeleton-based action recognition. Neurocomputing 537:198\u2013209","journal-title":"Neurocomputing"},{"key":"3263_CR38","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1109\/TMM.2023.3271811","volume":"26","author":"J Liu","year":"2023","unstructured":"Liu J, Wang X, Wang C, Gao Y, Liu M (2023) Temporal decoupling graph convolutional network for skeleton-based gesture recognition. IEEE Trans Multimed 26:811\u2013823","journal-title":"IEEE Trans Multimed"},{"key":"3263_CR39","doi-asserted-by":"crossref","unstructured":"Wang C, Jiang L, Wu X, Tian Z, Peng B, Zhao H, Jia J (2024) Groupcontrast: semantic-aware self-supervised representation learning for 3d understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 4917\u20134928","DOI":"10.1109\/CVPR52733.2024.00470"},{"key":"3263_CR40","first-page":"13918","volume":"38","author":"J Liu","year":"2024","unstructured":"Liu J, Chen S (2024) Timesurl: self-supervised contrastive learning for universal time series representation learning. Proc AAAI Conf Artif Intell 38:13918\u201313926","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"3263_CR41","doi-asserted-by":"crossref","unstructured":"Lin L, Wu L, Zhang J, Liu J (2024) Idempotent unsupervised representation learning for skeleton-based action recognition. European Conference on Computer Vision. Springer, pp 75\u201392","DOI":"10.1007\/978-3-031-73347-5_5"},{"key":"3263_CR42","doi-asserted-by":"crossref","unstructured":"Zhu Y, Han H, Yu Z, Liu G (2023) Modeling the relative visual tempo for self-supervised skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 13913\u201313922","DOI":"10.1109\/ICCV51070.2023.01279"},{"key":"3263_CR43","doi-asserted-by":"crossref","unstructured":"Shah A, Roy A, Shah K, Mishra S, Jacobs D, Cherian A, Chellappa R (2023) Halp: hallucinating latent positives for skeleton-based self-supervised learning of actions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 18846\u201318856","DOI":"10.1109\/CVPR52729.2023.01807"},{"key":"3263_CR44","doi-asserted-by":"crossref","unstructured":"Lin L, Zhang J, Liu J (2023) Actionlet-dependent contrastive learning for unsupervised skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 2363\u20132372","DOI":"10.1109\/CVPR52729.2023.00234"},{"key":"3263_CR45","doi-asserted-by":"crossref","unstructured":"Wang H, Ma X, Kuang J, Gui J (2025) Heterogeneous skeleton-based action representation learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp 19154\u201319164","DOI":"10.1109\/CVPR52734.2025.01784"},{"key":"3263_CR46","first-page":"8332","volume":"39","author":"W Weng","year":"2025","unstructured":"Weng W, Wang H, Wang J, He L, Xie G-S (2025) Usdrl: unified skeleton-based dense representation learning with multi-grained feature decorrelation. Proc AAAI Conf Artif Intell 39:8332\u20138340","journal-title":"Proc AAAI Conf Artif Intell"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03263-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03263-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03263-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:28:54Z","timestamp":1792758534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03263-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,14]]},"references-count":46,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["3263"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03263-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,14]]},"assertion":[{"value":"31 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"429"}}