{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T15:58:38Z","timestamp":1780329518779,"version":"3.54.1"},"reference-count":130,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61936003"],"award-info":[{"award-number":["61936003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771199"],"award-info":[{"award-number":["61771199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2017A030312006"],"award-info":[{"award-number":["2017A030312006"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2018A030313295"],"award-info":[{"award-number":["2018A030313295"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["x2dxD2190570"],"award-info":[{"award-number":["x2dxD2190570"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Guangzhou","award":["2018-1002-SF-0561"],"award-info":[{"award-number":["2018-1002-SF-0561"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1109\/tai.2021.3076974","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T16:02:42Z","timestamp":1625068962000},"page":"128-145","source":"Crossref","is-referenced-by-count":140,"title":["Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey"],"prefix":"10.1109","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8108-7915","authenticated-orcid":false,"given":"Tasweer","family":"Ahmad","sequence":"first","affiliation":[{"name":"School of Electronics, and Information Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-0957","authenticated-orcid":false,"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electronics, and Information Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1583-6401","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics, and Information Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1141-2487","authenticated-orcid":false,"given":"Songxuan","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electronics, and Information Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guozhi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronics, and Information Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luojun","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Fuzhou University, Fuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.12.007"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-5403"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.441"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICICT.2017.8320156"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/s19051005"},{"key":"ref30","article-title":"A survey on 3d skeleton-based action recognition using learning method","author":"ren","year":"0"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.122"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2017.12.003"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.02.030"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3338533.3366569"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1186\/s40649-019-0069-y"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2925285"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357924"},{"key":"ref22","first-page":"7444","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","author":"yan","year":"0","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref21","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"0","journal-title":"Proc 5th Int Conf Learn Representations"},{"key":"ref24","article-title":"Representation learning on graphs: Methods and applications","author":"hamilton","year":"0"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2935173"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851767"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3363574"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/SmartIoT.2019.00093"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5652"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_25"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2985219"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107511"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2019.115776"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351170"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108170"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00216"},{"key":"ref40","article-title":"Centrality graph convolutional networks for skeleton-based action recognition","author":"yang","year":"0"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.486"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2712608"},{"key":"ref6","first-page":"3697","article-title":"Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks","volume":"30","author":"zhu","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8546012"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"ref7","article-title":"Towards good practices for very deep two-stream convnets","author":"wang","year":"0"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093368"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00151"},{"key":"ref46","article-title":"Part-based graph convolutional network for action recognition","author":"thakkar","year":"0"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018561"},{"key":"ref48","article-title":"Spatio-temporal graph convolution for skeleton based action recognition","author":"chaolong","year":"0"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.096"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2879624"},{"key":"ref41","article-title":"ChebNet: Efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations","author":"tang","year":"0"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref43","article-title":"Bridging the gap between spectral and spatial domains in graph neural networks","author":"balcilar","year":"0"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.115"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.387"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_50"},{"key":"ref124","article-title":"A fine-to-coarse convolutional neural network for 3d human action recognition","author":"le","year":"2018"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.4108\/eai.21-6-2018.2276579"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58586-0_32"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3061115"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69541-5_3"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.233"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2964115"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00698"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/3369318.3369325"},{"key":"ref130","article-title":"Deep independently recurrent neural network (INDRNN","author":"li","year":"2019"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00078"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093598"},{"key":"ref78","article-title":"Videograph: Recognizing minutes-long human activities in videos","author":"hussein","year":"0"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2930344"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_9"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2961770"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2815744"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107321"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00132"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018989"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1126\/science.aad9029"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498247"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-08611-4"},{"key":"ref2","article-title":"Two-stream convolutional networks for action recognition in videos","volume":"27","author":"simonyan","year":"2014","journal-title":"Adv in Neural Info Proc Syst"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6759"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.675"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3053765"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2929257"},{"key":"ref108","article-title":"The kinetics human action video dataset","author":"kay","year":"0"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2974323"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.115"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3014445"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_40"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2996779"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1063\/1.5123119"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802917"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.05.005"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.3390\/app10041482"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01434"},{"key":"ref102","article-title":"Human action recognition with multi-Laplacian graph convolutional networks","author":"mazari","year":"0"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299172"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.339"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2916873"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093361"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00558"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00371"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00029"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8546165"},{"key":"ref11","article-title":"View adaptive neural networks for high performance skeleton-based human action recognition","volume":"41","author":"zhang","year":"1963","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093618"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/DDCLS.2018.8516099"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48308-5_54"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref16","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref118","author":"deepmind","year":"2020","journal-title":"Graph nets library"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939825"},{"key":"ref117","article-title":"Relational inductive biases, deep learning, and graph networks","author":"battaglia","year":"0"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/VCIP47243.2019.8965768"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/CCDC49329.2020.9163951"},{"key":"ref19","first-page":"4438","article-title":"An end-to-end deep learning architecture for graph classification","author":"zhang","year":"0","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref119","year":"2021","journal-title":"Jraph&#x2014;A Library for Graph Neural Networks in Jax"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"ref113","first-page":"2466","article-title":"Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations","author":"hussein","year":"0","journal-title":"Proc 23rd Int Joint Conf Artif Intell"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_7"},{"key":"ref116","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"wang","year":"0"},{"key":"ref115","first-page":"1","article-title":"Fast graph representation learning with PyTorch geometric","author":"fey","year":"0","journal-title":"Proc ICLR Workshop Representation Learn Graphs Manifolds"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/IUCC\/DSCI\/SmartCNS.2019.00074"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00054"},{"key":"ref121","first-page":"1117","article-title":"Temporal 3d convnets using temporal transition layer","author":"diba","year":"0","journal-title":"Proc IEEE Conf Comp Vis Pattern Recognit"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.21236\/ADA623249"},{"key":"ref123","article-title":"Graph attention networks","author":"veli?kovi?","year":"0"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00810"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018303"},{"key":"ref87","article-title":"Learning to model relationships for zero-shot video classification","author":"gao","year":"2020","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2973301"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/9523782\/09420299.pdf?arnumber=9420299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:56Z","timestamp":1755911396000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9420299\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4]]},"references-count":130,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tai.2021.3076974","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4]]}}}