{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:15:46Z","timestamp":1783721746675,"version":"3.55.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"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":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06531-w","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:02:10Z","timestamp":1729062130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-scale spatiotemporal topology unveiled: enhancing skeleton-based action recognition"],"prefix":"10.1007","volume":"81","author":[{"given":"Hongwei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianpeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zexi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"6531_CR1","doi-asserted-by":"crossref","unstructured":"Zhang P, Lan C, Zeng W, Xing J, Xue J, Zheng N (2020) Semantics-guided neural networks for efficient skeleton-based human action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1112\u20131121","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"6531_CR2","doi-asserted-by":"crossref","unstructured":"Hua Y, Wu W, Zheng C, Lu A, Liu M, Chen C, Wu S (2023) Part aware contrastive learning for self-supervised action recognition. arXiv preprint arXiv:2305.00666","DOI":"10.24963\/ijcai.2023\/95"},{"key":"6531_CR3","doi-asserted-by":"crossref","unstructured":"Liu D, Chen P, Yao M, Lu Y, Cai Z, Tian Y (2023) Tsgcnext: Dynamic-static multi-graph convolution for efficient skeleton-based action recognition with long-term learning potential. arXiv preprint arXiv:2304.11631","DOI":"10.2139\/ssrn.4984425"},{"issue":"4","key":"6531_CR4","doi-asserted-by":"publisher","first-page":"4592","DOI":"10.1007\/s10489-022-03589-y","volume":"53","author":"Y Xing","year":"2023","unstructured":"Xing Y, Zhu J, Li Y, Huang J, Song J (2023) An improved spatial temporal graph convolutional network for robust skeleton-based action recognition. Appl Intell 53(4):4592\u20134608","journal-title":"Appl Intell"},{"key":"6531_CR5","doi-asserted-by":"crossref","unstructured":"Zhou H, Liu Q, Wang Y (2023) Learning discriminative representations for skeleton based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 10608\u201310617","DOI":"10.1109\/CVPR52729.2023.01022"},{"key":"6531_CR6","doi-asserted-by":"crossref","unstructured":"Lee J, Lee M, Cho S, Woo S, Jang S, Lee S (2023) Leveraging spatio-temporal dependency for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10255\u201310264","DOI":"10.1109\/ICCV51070.2023.00941"},{"key":"6531_CR7","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":"6531_CR8","doi-asserted-by":"publisher","first-page":"109231","DOI":"10.1016\/j.patcog.2022.109231","volume":"136","author":"L Wu","year":"2023","unstructured":"Wu L, Zhang C, Zou Y (2023) Spatiotemporal focus for skeleton-based action recognition. Pattern Recogn 136:109231","journal-title":"Pattern Recogn"},{"key":"6531_CR9","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":"6531_CR10","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN (2017) L. u. Kaiser, and I. Polosukhin, attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"6531_CR11","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"6531_CR12","doi-asserted-by":"crossref","unstructured":"bibitemr12 Caetano C, Sena J, Br\u00e9mond F, Dos\u00a0Santos JA, Schwartz WR (2019) Skelemotion: a new representation of skeleton joint sequences based on motion information for 3d action recognition. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, pp 1\u20138","DOI":"10.1109\/AVSS.2019.8909840"},{"key":"6531_CR13","unstructured":"Joze HRV, Shaban A, Iuzzolino ML, Koishida K (2020) Mmtm: multimodal transfer module for CNN fusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 13289\u201313299"},{"key":"6531_CR14","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2020) Decoupled spatial-temporal attention network for skeleton-based action-gesture recognition. In: Proceedings of the Asian Conference on Computer Vision","DOI":"10.1007\/978-3-030-69541-5_3"},{"key":"6531_CR15","doi-asserted-by":"crossref","unstructured":"Luo J, Zhou L, Zhu G, Ge G, Yang B, Wang J (2023) Temporal-channel topology enhanced network for skeleton-based action recognition. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Springer, pp 109\u2013119","DOI":"10.1007\/978-981-99-8429-9_9"},{"key":"6531_CR16","doi-asserted-by":"crossref","unstructured":"Duan H, Xu M, Shuai B, Modolo D, Tu Z, Tighe J, Bergamo A (2023) Skeletr: towards skeleton-based action recognition in the wild. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 13634\u201313644","DOI":"10.1109\/ICCV51070.2023.01254"},{"key":"6531_CR17","doi-asserted-by":"crossref","unstructured":"Wang L, Koniusz P (2023) 3mformer: multi-order multi-mode transformer for skeletal action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 5620\u20135631","DOI":"10.1109\/CVPR52729.2023.00544"},{"key":"6531_CR18","unstructured":"Do J, Kim M (2024) Skateformer: skeletal-temporal transformer for human action recognition. arXiv preprint arXiv:2403.09508"},{"key":"6531_CR19","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"6531_CR20","doi-asserted-by":"crossref","unstructured":"Li M, Chen S, Chen X, Zhang Y, Wang Y, Tian Q (2019) Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 3595\u20133603","DOI":"10.1109\/CVPR.2019.00371"},{"issue":"5","key":"6531_CR21","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TCSVT.2020.3015051","volume":"31","author":"Y-F Song","year":"2020","unstructured":"Song Y-F, Zhang Z, Shan C, Wang L (2020) Richly activated graph convolutional network for robust skeleton-based action recognition. IEEE Trans Circuits Syst Video Technol 31(5):1915\u20131925","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6531_CR22","doi-asserted-by":"crossref","unstructured":"Liu Z, Zhang H, Chen Z, Wang Z, Ouyang W (2020) Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 143\u2013152","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"6531_CR23","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"6531_CR24","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C (2020) X3d: expanding architectures for efficient video recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 203\u2013213","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"6531_CR25","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Fan H, Malik J, He K (2019) Slowfast networks for video recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 6202\u20136211","DOI":"10.1109\/ICCV.2019.00630"},{"key":"6531_CR26","doi-asserted-by":"crossref","unstructured":"Duan H, Zhao Y, Chen K, Lin D, Dai B (2022) Revisiting skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2969\u20132978","DOI":"10.1109\/CVPR52688.2022.00298"},{"key":"6531_CR27","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"6531_CR28","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"6531_CR29","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"6531_CR30","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng T-T, Wang G (2016) Ntu rgb+ d: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1010\u20131019","DOI":"10.1109\/CVPR.2016.115"},{"key":"6531_CR31","doi-asserted-by":"crossref","unstructured":"Shao D, Zhao Y, Dai B, Lin D (2020) Finegym: a hierarchical video dataset for fine-grained action understanding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2616\u20132625","DOI":"10.1109\/CVPR42600.2020.00269"},{"issue":"8","key":"6531_CR32","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/TPAMI.2019.2896631","volume":"41","author":"P Zhang","year":"2019","unstructured":"Zhang P, Lan C, Xing J, Zeng W, Xue J, Zheng N (2019) View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans Pattern Anal Mach Intell 41(8):1963\u20131978","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6531_CR33","first-page":"2866","volume":"36","author":"K Xu","year":"2022","unstructured":"Xu K, Ye F, Zhong Q, Xie D (2022) Topology-aware convolutional neural network for efficient skeleton-based action recognition. Proc AAAI Conf Artif Intell 36:2866\u20132874","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"3","key":"6531_CR34","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s10044-023-01156-w","volume":"26","author":"Q Cheng","year":"2023","unstructured":"Cheng Q, Cheng J, Ren Z, Zhang Q, Liu J (2023) Multi-scale spatial-temporal convolutional neural network for skeleton-based action recognition. Pattern Anal Appl 26(3):1303\u20131315","journal-title":"Pattern Anal Appl"},{"key":"6531_CR35","unstructured":"Cai D, Kang Y, Yao A, Chen Y (2023) Ske2grid: skeleton-to-grid representation learning for action recognition. In: International Conference on Machine Learning, PMLR, pp 3431\u20133441"},{"key":"6531_CR36","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7912\u20137921","DOI":"10.1109\/CVPR.2019.00810"},{"key":"6531_CR37","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2021) Adasgn: adapting joint number and model size for efficient skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 13413\u201313422","DOI":"10.1109\/ICCV48922.2021.01316"},{"key":"6531_CR38","doi-asserted-by":"publisher","first-page":"109540","DOI":"10.1016\/j.patcog.2023.109540","volume":"140","author":"M Dai","year":"2023","unstructured":"Dai M, Sun Z, Wang T, Feng J, Jia K (2023) Global spatio-temporal synergistic topology learning for skeleton-based action recognition. Pattern Recogn 140:109540","journal-title":"Pattern Recogn"},{"issue":"2","key":"6531_CR39","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","volume":"45","author":"Y-F Song","year":"2022","unstructured":"Song Y-F, Zhang Z, Shan C, Wang L (2022) Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans Pattern Anal Mach Intell 45(2):1474\u20131488","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6531_CR40","doi-asserted-by":"crossref","unstructured":"Xu Z, Xu J (2024) Gr-former: Graph-reinforcement transformer for skeleton-based driver action recognition. IET Computer Vision","DOI":"10.1049\/cvi2.12298"},{"issue":"1","key":"6531_CR41","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s00530-023-01251-2","volume":"30","author":"H Cui","year":"2024","unstructured":"Cui H, Hayama T (2024) STSD: spatial-temporal semantic decomposition transformer for skeleton-based action recognition. Multimedia Syst 30(1):43","journal-title":"Multimedia Syst"},{"key":"6531_CR42","doi-asserted-by":"publisher","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","volume":"29","author":"L Shi","year":"2020","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2020) Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans Image Process 29:9532\u20139545","journal-title":"IEEE Trans Image Process"},{"key":"6531_CR43","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"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06531-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06531-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06531-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:05:41Z","timestamp":1729062341000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06531-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6531"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06531-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]},"assertion":[{"value":"17 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study does not involve human or animal subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human or animal participation"}}],"article-number":"10"}}