{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:58:32Z","timestamp":1770742712768,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61672305"],"award-info":[{"award-number":["61672305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61672305"],"award-info":[{"award-number":["61672305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10044-024-01319-3","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T22:45:48Z","timestamp":1726785948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on decoupled adaptive graph convolution networks based on skeleton data for action recognition"],"prefix":"10.1007","volume":"27","author":[{"given":"Haigang","family":"Deng","sequence":"first","affiliation":[]},{"given":"Guocheng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chengwei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chuanxu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"1319_CR1","doi-asserted-by":"crossref","unstructured":"Huang J, Xiang X, Gong X, Zhang B (2020) Long-short graph memory network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 645\u2013652","DOI":"10.1109\/WACV45572.2020.9093598"},{"key":"1319_CR2","unstructured":"Sheng L, Tingting J, Tiejun H, Yonghong T (2020) Global co-occurrence feature learning and active coordinate system conversion for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). pp 586\u201359416"},{"key":"1319_CR3","doi-asserted-by":"crossref","unstructured":"Du Y, Fu Y and Wang L (2015) Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) IEEE, pp 579\u2013583","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"1319_CR4","doi-asserted-by":"crossref","unstructured":"Li C, Zhong Q, Xie D and Pu S (2017) Skeleton-based action recognition with convolutional neural networks. In: 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) IEEE, pp 597\u2013600","DOI":"10.1109\/ICMEW.2017.8026285"},{"key":"1319_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2020.07.068","volume":"414","author":"A Zhu","year":"2020","unstructured":"Zhu A, Wu Q, Cui R, Wang T, Hang W, Hua GAND, Snoussi H (2020) Exploring a rich spatial\u2013temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN. Neurocomputing 414:90\u2013100","journal-title":"Neurocomputing"},{"key":"1319_CR6","doi-asserted-by":"crossref","unstructured":"Papadopoulos K, Ghorbel E, Aouada D et al. (2021) Vertex feature encoding and hierarchical temporal modeling in a spatio-temporal graph convolutional network for action recognition. In: 25th International Conference on Pattern Recognition (ICPR). IEEE, pp 452\u2013458","DOI":"10.1109\/ICPR48806.2021.9413189"},{"key":"1319_CR7","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Z, Cheng J and Lu H (2019) Two stream adaptive graph convolutional networks for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 12026\u201312035","DOI":"10.1109\/CVPR.2019.01230"},{"key":"1319_CR8","doi-asserted-by":"crossref","unstructured":"Cheng K, Zhang Y, Cao C, Shi L, Cheng J and Lu H (2020) Decoupling gcn with dropgraph module for skeleton-based action recognition. In: Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-58586-0_32"},{"key":"1319_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58586-0_32","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.1007\/978-3-030-58586-0_32","journal-title":"Adv Neural Inf Process Syst"},{"key":"1319_CR10","doi-asserted-by":"publisher","first-page":"103219","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari C, Cannici M, Matteucci M (2021) Skeleton-based action recognition via spatial and temporal transformer networks. Comput Vis Image Underst 208:103219","journal-title":"Comput Vis Image Underst"},{"key":"1319_CR11","unstructured":"Wang Q, Peng J, Shi S et al. (2021) Iip-transformer: Intra-inter-part transformer for skeleton-based action recognition. arXiv preprint arXiv:2110.13385"},{"key":"1319_CR12","doi-asserted-by":"publisher","unstructured":"Sekaran RS, Pang YH, Ling GF et al. (2022) MSTCN: a multiscale temporal convolutional network for user independent human activity recognition. F1000Research. https:\/\/doi.org\/10.12688\/f1000research.73175.2","DOI":"10.12688\/f1000research.73175.2"},{"key":"1319_CR13","doi-asserted-by":"crossref","unstructured":"Du Y, Fu Y, Wang L (2015) Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, pp 579\u2013583","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"1319_CR14","doi-asserted-by":"crossref","unstructured":"Wang P, Li Z, Hou Y et al. (2016) Action recognition based on joint trajectory maps using convolutional neural networks. In: Proceedings of the 24th ACM international conference on Multimedia. pp 102\u2013106","DOI":"10.1145\/2964284.2967191"},{"issue":"11","key":"1319_CR15","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1109\/TMM.2019.2962304","volume":"22","author":"K Zhu","year":"2020","unstructured":"Zhu K, Wang R, Zhao Q, Cheng J, Tao D (2020) A cuboid CNN model with an attention mechanism for skeleton-based action recognition. IEEE Trans Multimedia 22(11):2977\u20132989. https:\/\/doi.org\/10.1109\/TMM.2019.2962304","journal-title":"IEEE Trans Multimedia"},{"key":"1319_CR16","doi-asserted-by":"crossref","unstructured":"Du Y, Wang W and Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1110\u20131118","DOI":"10.1109\/CVPR.2015.7298714"},{"issue":"4","key":"1319_CR17","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TIP.2017.2785279","volume":"27","author":"J Liu","year":"2017","unstructured":"Liu J, Wang G, Duan L-Y, Abdiyeva KAND, Kot AC (2017) Skeleton-based human action recognition with global contextaware attention LSTM networks. IEEE Trans Image Process 27(4):1586\u20131599","journal-title":"IEEE Trans Image Process"},{"key":"1319_CR18","doi-asserted-by":"crossref","unstructured":"Wei S, Song Y and Zhang Y (2017, September) Human skeleton tree recurrent neural network with joint relative motion feature for skeleton based action recognition. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 91\u201395","DOI":"10.1109\/ICIP.2017.8296249"},{"key":"1319_CR19","doi-asserted-by":"crossref","unstructured":"Si C, Chen W, Wang W, Wang L and Tan T (2019) An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1227\u20131236","DOI":"10.1109\/CVPR.2019.00132"},{"key":"1319_CR20","doi-asserted-by":"crossref","unstructured":"Sijie S, Xiong Y and Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI conference on artificial intelligence. vol 32. no 1","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"1319_CR21","doi-asserted-by":"crossref","unstructured":"Lee J, Lee M, Lee D et al. (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":"1319_CR22","unstructured":"Yang Z, Li K, Gan H et al. (2023) HD-GCN: A Hybrid Diffusion Graph Convolutional Network. arXiv preprint arXiv:2303.17966"},{"key":"1319_CR23","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wu B, Li W et al. (2021) STST: Spatial-temporal specialized transformer for skeleton-based action recognition.In: Proceedings of the 29th ACM International Conference on Multimedia. pp 3229\u20133237","DOI":"10.1145\/3474085.3475473"},{"key":"1319_CR24","unstructured":"Wei J, Wang Y, Guo M, et al. (2021) Dynamic hypergraph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv: 2112.10570"},{"key":"1319_CR25","unstructured":"Haodong D et al. (2022) DG-STGCN: dynamic spatial-temporal modeling for skeleton-based action recognition. arXiv preprint arXiv:2210.05895"},{"key":"1319_CR26","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 et al (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":"1319_CR27","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J et al. (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":"1319_CR28","doi-asserted-by":"crossref","unstructured":"Liu Z, Zhang H, Chen Z, Wang Z and Ouyang W (2020) MS-G3D: 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":"1319_CR29","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J,Ng T-T and Wang G (June 2016) Ntu rgb+d: a large scale dataset for 3d human activity analysis. In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2016.115"},{"key":"1319_CR30","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","volume":"42","author":"J Liu","year":"2019","unstructured":"Liu J, Shahroudy A, Perez ML, Wang G, Duan L-Y, Chichung AK (2019) Ntu rgb+d 120: a large-scale benchmark for 3d human activity understanding. IEEE Trans Pattern Anal Mach Intell 42:2684","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"1319_CR31","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1109\/TPAMI.2013.198","volume":"36","author":"J Wang","year":"2013","unstructured":"Wang J, Liu Z, Ying Wu, Yuan J (2013) Learning actionlet ensemble for 3D human action recognition. IEEE Trans Pattern Anal Mach Intell 36(5):914\u2013927","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1319_CR32","doi-asserted-by":"crossref","unstructured":"Li S, Li W, Cook C, Zhu C and Gao Y (2018) Independently recurrent neural network (indrnn): building a longer and deeper rnn. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5457\u20135466","DOI":"10.1109\/CVPR.2018.00572"},{"key":"1319_CR33","doi-asserted-by":"crossref","unstructured":"Li C, Zhong Q, Xie D et al. (2018) Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. arXiv preprint arXiv:1804.06055, pp 786\u2013792","DOI":"10.24963\/ijcai.2018\/109"},{"key":"1319_CR34","doi-asserted-by":"crossref","unstructured":"Zhang P, Lan C, Zeng W, Xing J, Xue J and 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"},{"issue":"14","key":"1319_CR35","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.3390\/electronics12143156","volume":"12","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Yu S, Wang T, Sun Z, Wang S (2023) Skeleton-based human action recognition based on single path one-shot neural architecture search. Electronics 12(14):3156","journal-title":"Electronics"},{"key":"1319_CR36","doi-asserted-by":"publisher","first-page":"107210","DOI":"10.1016\/j.engappai.2023.107210","volume":"127","author":"X Yu","year":"2024","unstructured":"Yu X et al (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"},{"key":"1319_CR37","doi-asserted-by":"crossref","unstructured":"Cheng K, Zhang Y, He X, Chen W, Cheng J and Lu H (2020) Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 183\u2013192","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"1319_CR38","doi-asserted-by":"crossref","unstructured":"Song Y-F, Zhang Z, Shan C, and Wang L (2020) Stronger, faster and more explainable: a graph convolutional baseline for skeleton-based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia. pp 1625\u20131633","DOI":"10.1145\/3394171.3413802"},{"key":"1319_CR39","doi-asserted-by":"crossref","unstructured":"Ye F, Pu S, Zhong Q, Li C, Xie D and Tang H (2020) Dynamic gcn: context-enriched topology learning for skeleton-based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia. pp 55\u201363","DOI":"10.1145\/3394171.3413941"},{"key":"1319_CR40","doi-asserted-by":"crossref","unstructured":"Shu Y, Li W, Li D, Gao K, and Jie B (2023, October) Multi-scale dilated attention graph convolutional network for skeleton-based action recognition. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer Nature Singapore, Singapore. pp 16\u201328","DOI":"10.1007\/978-981-99-8429-9_2"},{"key":"1319_CR41","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","volume":"45","author":"YF Ong","year":"2023","unstructured":"Ong YF, Zhang Z, Shan C et al (2023) Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans Pattern Anal Mach Intell 45:1474\u20131488","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"1319_CR42","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TVCG.2023.3247075","volume":"29","author":"Y Liu","year":"2023","unstructured":"Liu Y, Zhang H, Li Y, He K, Xu D (2023) Skeleton-based human action recognition via large-kernel attention graph convolutional network. IEEE Trans Visual Comput Graph 29(5):2575\u20132585","journal-title":"IEEE Trans Visual Comput Graph"},{"key":"1319_CR43","doi-asserted-by":"publisher","first-page":"110188","DOI":"10.1016\/j.patcog.2023.110188","volume":"148","author":"H Qiu","year":"2024","unstructured":"Qiu H, Hou B (2024) Multi-grained clip focus for skeleton-based action recognition. Pattern Recogn 148:110188","journal-title":"Pattern Recogn"},{"key":"1319_CR44","doi-asserted-by":"publisher","unstructured":"Jang S, Lee H, Kim WJ, Lee J, Woo S and Lee S (2024) Multi-scale structural graph convolutional network for skeleton-based action recognition. In: IEEE transactions on circuits and systems for video technology. https:\/\/doi.org\/10.1109\/TCSVT.2024.3375512","DOI":"10.1109\/TCSVT.2024.3375512"},{"issue":"2","key":"1319_CR45","first-page":"1113","volume":"35","author":"Z Chen","year":"2021","unstructured":"Chen Z, Li S, Yang B et al (2021) Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition. Proc AAAI Conf Artif Intell 35(2):1113\u20131122","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1319_CR46","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang Z, Yuan C, et al. (2021) Channel-wise topology refinement graph convolution for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 13359\u201313368","DOI":"10.1109\/ICCV48922.2021.01311"},{"issue":"3","key":"1319_CR47","first-page":"2866","volume":"36","author":"K Xu","year":"2022","unstructured":"Xu K, Ye F, Zhong Q et al (2022) Topology-aware convolutional neural network for efficient skeleton-based action recognition. Proc AAAI Conf Artif Intell 36(3):2866\u20132874","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1319_CR48","doi-asserted-by":"crossref","unstructured":"Gao Z, Wang P, Lv P, Jiang X, Liu Q, Wang P and Li W (2022) Focal and global spatial-temporal transformer for skeleton-based action recognition. In: Proceedings of the Asian Conference on Computer Vision. pp 382\u2013398","DOI":"10.1007\/978-3-031-26316-3_10"},{"key":"1319_CR49","doi-asserted-by":"crossref","unstructured":"Chi H, Ha M- H, Chi S et al. (2022) Infogcn: representation learning for human skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 20186\u201320196","DOI":"10.1109\/CVPR52688.2022.01955"},{"key":"1319_CR50","doi-asserted-by":"publisher","first-page":"109540","DOI":"10.1016\/j.patcog.2023.109540","volume":"140","author":"M Dai","year":"2023","unstructured":"Dai M et al (2023) Global spatio-temporal synergistic topology learning for skeleton-based action recognition. Pattern Recognition 140:109540","journal-title":"Pattern Recognition"},{"key":"1319_CR51","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/TMM.2020.2978637","volume":"23","author":"I Lee","year":"2021","unstructured":"Lee I, Kim D, Lee S (2021) 3-D human behavior understanding using generalized TS-LSTM networks. IEEE Trans Multimed 23:415\u2013428. https:\/\/doi.org\/10.1109\/TMM.2020.2978637","journal-title":"IEEE Trans Multimed"},{"key":"1319_CR52","doi-asserted-by":"crossref","unstructured":"Hu H et al. (2024) Multi-scale Adaptive Graph Convolution Network for Skeleton-based Action Recognition. IEEE Access","DOI":"10.1109\/ACCESS.2024.3359234"},{"key":"1319_CR53","unstructured":"Yu Z et al. (2024) Cross-scale spatiotemporal refinement learning for skeleton-based action recognition. IEEE signal processing letters"},{"key":"1319_CR54","doi-asserted-by":"crossref","unstructured":"Zhou H, Liu Q and 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"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01319-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-024-01319-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01319-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T09:28:15Z","timestamp":1734341295000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-024-01319-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,18]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1319"],"URL":"https:\/\/doi.org\/10.1007\/s10044-024-01319-3","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,18]]},"assertion":[{"value":"11 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2024","order":3,"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 that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"118"}}