{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T22:01:00Z","timestamp":1768082460395,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20180640"],"award-info":[{"award-number":["BK20180640"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902404"],"award-info":[{"award-number":["61902404"]}],"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":["51734009"],"award-info":[{"award-number":["51734009"]}],"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":["61771417"],"award-info":[{"award-number":["61771417"]}],"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":["61873246"],"award-info":[{"award-number":["61873246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the State Key Research Development Program","award":["2016YFC0801403"],"award-info":[{"award-number":["2016YFC0801403"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["512918914"],"award-info":[{"award-number":["512918914"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02370-x","type":"journal-article","created":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T06:49:58Z","timestamp":1619333398000},"page":"113-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Triplet attention multiple spacetime-semantic graph convolutional network for skeleton-based action recognition"],"prefix":"10.1007","volume":"52","author":[{"given":"Yanjing","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1538-5279","authenticated-orcid":false,"given":"Xiao","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,24]]},"reference":[{"issue":"11","key":"2370_CR1","doi-asserted-by":"publisher","first-page":"3247","DOI":"10.1109\/TCSVT.2018.2879913","volume":"29","author":"C Cao","year":"2018","unstructured":"Cao C, Lan C, Zhang Y, Zeng W, Lu H, Zhang Y (2018) Skeleton-based action recognition with gated convolutional neural networks. IEEE Trans Circuits Syst Video Technol 29(11):3247\u20133257","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2370_CR2","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291\u20137299","DOI":"10.1109\/CVPR.2017.143"},{"key":"2370_CR3","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":"2370_CR4","doi-asserted-by":"crossref","unstructured":"Chen Y, Ma G, Yuan C, Li B, Zhang H, Wang F, Hu W (2020) Graph convolutional network with structure pooling and joint-wise channel attention for action recognition. Pattern Recognit, 103","DOI":"10.1016\/j.patcog.2020.107321"},{"issue":"5","key":"2370_CR5","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s10489-020-01803-3","volume":"51","author":"C Ding","year":"2021","unstructured":"Ding C, Liu K, Cheng F, Belyaev E (2021) Spatio-temporal attention on manifold space for 3d human action recognition. Appl Intell 51(5):560\u2013570","journal-title":"Appl Intell"},{"key":"2370_CR6","unstructured":"Du Y, Wang W, 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"},{"key":"2370_CR7","unstructured":"Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Conference and workshop on neural information processing systems, pp 2224\u20132232"},{"issue":"3","key":"2370_CR8","first-page":"1","volume":"50","author":"Y Feng","year":"2020","unstructured":"Feng Y, Li K, Gao Y, Qiu J (2020) Hierarchical graph attention networks for semi-supervised node classification. Appl Intell 50(3):1\u201317","journal-title":"Appl Intell"},{"key":"2370_CR9","doi-asserted-by":"crossref","unstructured":"Fernando B, Gavves E, Oramas JM, Ghodrati A, Tuytelaars T (2015) Modeling video evolution for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5378\u20135387","DOI":"10.1109\/CVPR.2015.7299176"},{"key":"2370_CR10","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 conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"2370_CR11","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ins.2019.12.084","volume":"517","author":"P Gao","year":"2020","unstructured":"Gao P, Zhang Q, Wang F, Xiao L, Zhang Y (2020) Learning reinforced attentional representation for end-to-end visual tracking. Inf Sci 517:52\u201367","journal-title":"Inf Sci"},{"key":"2370_CR12","doi-asserted-by":"crossref","unstructured":"Gaur U, Zhu Y, Song B, Roy-Chowdhury A (2011) A \u201cstring of feature graphs\u201d model for recognition of complex activities in natural videos. In: Proceedings of the IEEE 15th international conference on computer vision, pp 2595\u20132602","DOI":"10.1109\/ICCV.2011.6126548"},{"key":"2370_CR13","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Conference and workshop on neural information processing systems, pp 1024\u20131034"},{"key":"2370_CR14","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2370_CR15","unstructured":"Hussein ME, Torki M, Gowayyed MA, El-Saban M (2013) Human action recognition using a temporal hierarchy of covariance descriptors on 3d locations. In: International joint conference on artificial intelligence"},{"key":"2370_CR16","unstructured":"i R, Tapaswi M, Liao R, Jia J, Urtasun R, Fidler S (2017) Situation recognition with graph neural networks. In: IEEE International conference on computer vision, pp 4183\u20134192"},{"key":"2370_CR17","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, Viola F, Green T, Back T, Natsev P et al (2017) The kinetics human action video dataset. arXiv:1705.06950"},{"key":"2370_CR18","doi-asserted-by":"crossref","unstructured":"Ke Q, Bennamoun M, An S, Sohel F, Boussaid F (2017) A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3288\u20133297","DOI":"10.1109\/CVPR.2017.486"},{"issue":"6","key":"2370_CR19","doi-asserted-by":"publisher","first-page":"2842","DOI":"10.1109\/TIP.2018.2812099","volume":"27","author":"Q Ke","year":"2018","unstructured":"Ke Q, Bennamoun M, An S, Sohel F, Boussaid F (2018) Learning clip representations for skeleton-based 3d action recognition. IEEE Trans Image Process 27(6):2842\u20132855","journal-title":"IEEE Trans Image Process"},{"key":"2370_CR20","doi-asserted-by":"crossref","unstructured":"Kim TS, Reiter A (2017) Interpretable 3d human action analysis with temporal convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition Workshop, pp 1623\u20131631","DOI":"10.1109\/CVPRW.2017.207"},{"key":"2370_CR21","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations, pp 1\u201314"},{"key":"2370_CR22","doi-asserted-by":"crossref","unstructured":"Li C, Zhong Q, Xie D, Pu S (2018) Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. In: International joint conferences on artificial intelligence, pp 786\u2013792","DOI":"10.24963\/ijcai.2018\/109"},{"key":"2370_CR23","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 conference on computer vision and pattern recognition, pp 3595\u20133603","DOI":"10.1109\/CVPR.2019.00371"},{"key":"2370_CR24","doi-asserted-by":"crossref","unstructured":"Lin TY, 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":"2370_CR25","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal lstm with trust gates for 3d human action recognition. In: European conference on computer vision, pp 816\u2013833","DOI":"10.1007\/978-3-319-46487-9_50"},{"issue":"8","key":"2370_CR26","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patcog.2017.02.030","volume":"68","author":"M Liu","year":"2017","unstructured":"Liu M, Liu H, Chen C (2017) Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognit 68(8):346\u2013362","journal-title":"Pattern Recognit"},{"key":"2370_CR27","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: European conference on computer vision, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"2","key":"2370_CR28","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TMM.2019.2930344","volume":"22","author":"L Lu","year":"2020","unstructured":"Lu L, Yu R, Di H, Zhang L, Lu Y (2020) Gaim: Graph attention based interaction model for collective activity recognition. IEEE Trans Multimedia 22(2):524\u2013539","journal-title":"IEEE Trans Multimedia"},{"key":"2370_CR29","doi-asserted-by":"crossref","unstructured":"Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5115\u20135124","DOI":"10.1109\/CVPR.2017.576"},{"key":"2370_CR30","unstructured":"Niepert M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs. In: Proceedings of the 33rd international conference on machine learning and data mining, pp 2014\u20132023"},{"key":"2370_CR31","doi-asserted-by":"publisher","first-page":"100590","DOI":"10.1016\/j.knosys.2020.105590","volume":"194","author":"F P\u00e9rez-Hern\u00e1ndez","year":"2020","unstructured":"P\u00e9rez-Hern\u00e1ndez F, Tabik S, Lamas A, Olmos R, Herrera F (2020) Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance. Knowl-Based Syst 194:100590","journal-title":"Knowl-Based Syst"},{"key":"2370_CR32","doi-asserted-by":"crossref","unstructured":"Qi S, Wang W, Jia B, Shen J, Zhu SC (2018) Learning human-object interactions by graph parsing neural networks. In: European conference on computer vision, pp 401\u2013417","DOI":"10.1007\/978-3-030-01240-3_25"},{"key":"2370_CR33","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng TT, 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"},{"issue":"5","key":"2370_CR34","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TPAMI.2017.2691321","volume":"40","author":"A Shahroudy","year":"2018","unstructured":"Shahroudy A, Ng TT, Gong Y, Wang G (2018) Deep multimodal feature analysis for action recognition in rgb+d videos. IEEE Trans Pattern Anal Mach Intell 40(5):1045\u20131058","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2370_CR35","doi-asserted-by":"crossref","unstructured":"Shi L, Zhang Y, Cheng J, 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":"2370_CR36","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":"2370_CR37","doi-asserted-by":"crossref","unstructured":"Si C, Chen W, Wang W, Wang L, 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":"2370_CR38","doi-asserted-by":"crossref","unstructured":"Song S, Lan C, Xing J, Zeng W, Liu J (2017) An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Thirty-first AAAI conference on artificial intelligence, pp 4263\u20134270","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"2370_CR39","doi-asserted-by":"crossref","unstructured":"Tang Y, Tian Y, Lu J, Li P, Zhou J (2018) Deep progressive reinforcement learning for skeleton-based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5323\u20135332","DOI":"10.1109\/CVPR.2018.00558"},{"key":"2370_CR40","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN (2017) Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Conference and workshop on neural information processing systems, pp 5998\u20136008"},{"key":"2370_CR41","doi-asserted-by":"crossref","unstructured":"Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3d skeletons as points in a lie group. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 588\u2013595","DOI":"10.1109\/CVPR.2014.82"},{"key":"2370_CR42","doi-asserted-by":"crossref","unstructured":"Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1290\u20131297","DOI":"10.1109\/CVPR.2012.6247813"},{"key":"2370_CR43","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhou L, Qiao Y (2018) Temporal hallucinating for action recognition with few still images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5314\u20135322","DOI":"10.1109\/CVPR.2018.00557"},{"key":"2370_CR44","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, So Kweon I (2018) Cbam: Convolutional block attention module. In: European conference on computer vision, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2370_CR45","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligence, pp 7444\u20137452","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"2370_CR46","unstructured":"Yang D, Li MM, Fu H, Fan J, Leung H (2020) Centrality graph convolutional networks for skeleton-based action recognition. arXiv:2003.03007"},{"issue":"7","key":"2370_CR47","doi-asserted-by":"publisher","first-page":"10040","DOI":"10.1109\/ACCESS.2020.2964115","volume":"8","author":"H Yang","year":"2020","unstructured":"Yang H, Gu Y, Zhu J, Hu K, Zhang X (2020) Pgcn-tca: Pseudo graph convolutional network with temporal and channel-wise attention for skeleton-based action recognition. IEEE Access 8(7):10040\u201310047","journal-title":"IEEE Access"},{"key":"2370_CR48","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2018) Self-attention generative adversarial networks. arXiv:1805.08318"},{"issue":"9","key":"2370_CR49","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TMM.2018.2802648","volume":"20","author":"S Zhang","year":"2018","unstructured":"Zhang S, Yang Y, Xiao J, Liu X, Yang Y, Xie D, Zhuang Y (2018) Fusing geometric features for skeleton-based action recognition using multilayer lstm networks. IEEE Trans Multimed 20(9):2330\u20132343","journal-title":"IEEE Trans Multimed"},{"issue":"8","key":"2370_CR50","doi-asserted-by":"publisher","first-page":"3047","DOI":"10.1109\/TNNLS.2019.2935173","volume":"31","author":"X Zhang","year":"2020","unstructured":"Zhang X, Xu C, Tian X, Tao D (2020) Graph edge convolutional neural networks for skeleton-based action recognition. IEEE Trans Neural Netw Learn Syst 31(8):3047\u20133060","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02370-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02370-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02370-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T10:51:01Z","timestamp":1671965461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02370-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,24]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2370"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02370-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,24]]},"assertion":[{"value":"17 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}