{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:56:14Z","timestamp":1753354574275,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755875"},{"type":"electronic","value":"9789819755882"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5588-2_32","type":"book-chapter","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T18:02:48Z","timestamp":1723485768000},"page":"380-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SDE-Net: Skeleton Action Recognition Based on Spatio-Temporal Dependence Enhanced Networks"],"prefix":"10.1007","author":[{"given":"Qing","family":"Sun","sequence":"first","affiliation":[]},{"given":"Jiuzhen","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Zhou","family":"Xinwen","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7444\u20137452 (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patrec.2021.06.003","volume":"148","author":"Q Xu","year":"2021","unstructured":"Xu, Q., Zheng, W., Song, Y., et al.: Scene image and human skeleton-based dual-stream human action recognition. Pattern Recogn. Lett. 148, 136\u2013145 (2021)","journal-title":"Pattern Recogn. Lett."},{"issue":"11","key":"32_CR3","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1109\/TPAMI.2018.2868668","volume":"41","author":"L Wang","year":"2018","unstructured":"Wang, L., Xiong, Y., Wang, Z., et al.: Temporal segment networks for action recognition in videos. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2740\u20132755 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2018.07.028","volume":"85","author":"H Yang","year":"2019","unstructured":"Yang, H., Yuan, C., Li, B., et al.: Asymmetric 3D convolutional neural networks for action recognition. Pattern Recogn. 85, 1\u201312 (2019)","journal-title":"Pattern Recogn."},{"key":"32_CR5","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.knosys.2017.01.035","volume":"122","author":"X Ji","year":"2017","unstructured":"Ji, X., Cheng, J., Tao, D., et al.: The spatial Laplacian and temporal energy pyramid representation for human action recognition using depth sequences. Knowl.-Based Syst. 122, 64\u201374 (2017)","journal-title":"Knowl.-Based Syst."},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.ins.2018.12.050","volume":"480","author":"Y Xiao","year":"2019","unstructured":"Xiao, Y., Chen, J., Wang, Y., et al.: Action recognition for depth video using multi-view dynamic images. Inf. Sci. 480, 287\u2013304 (2019)","journal-title":"Inf. Sci."},{"key":"32_CR7","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.neucom.2020.12.020","volume":"433","author":"Z Ren","year":"2021","unstructured":"Ren, Z., Zhang, Q., Cheng, J., et al.: Segment spatial-temporal representation and cooperative learning of convolution neural networks for multimodal-based action recognition. Neurocomputing 433, 142\u2013153 (2021)","journal-title":"Neurocomputing"},{"issue":"7","key":"32_CR8","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1109\/LSP.2018.2841649","volume":"25","author":"Y Xu","year":"2018","unstructured":"Xu, Y., Cheng, J., Wang, L., et al.: Ensemble one-dimensional convolution neural networks for skeleton-based action recognition. IEEE Signal Process. Lett. 25(7), 1044\u20131048 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"32_CR9","doi-asserted-by":"publisher","first-page":"22901","DOI":"10.1007\/s11042-018-5642-0","volume":"77","author":"B Li","year":"2018","unstructured":"Li, B., He, M., Dai, Y., et al.: 3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN. Multimedia Tools Appl. 77, 22901\u201322921 (2018)","journal-title":"Multimedia Tools Appl."},{"issue":"6","key":"32_CR10","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., et al.: Learning clip representations for skeleton-based 3D action recognition. IEEE Trans. Image Process. 27(6), 2842\u20132855 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/978-3-319-46487-9_50","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Liu","year":"2016","unstructured":"Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal lstm with trust gates for 3d human action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 816\u2013833. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_50"},{"issue":"8","key":"32_CR12","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., et al.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1963\u20131978 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"32_CR13","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., et al.: Skeleton-based human action recognition with global context-aware attention LSTM networks. IEEE Trans. Image Process. 27(4), 1586\u20131599 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"32_CR14","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","volume":"45","author":"YF Song","year":"2022","unstructured":"Song, Y.F., Zhang, Z., Shan, C., et al.: Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1474\u20131488 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"32_CR15","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/s10489-022-03436-0","volume":"53","author":"W Yang","year":"2023","unstructured":"Yang, W., Zhang, J., Cai, J., et al.: HybridNet: integrating GCN and CNN for skeleton-based action recognition. Appl. Intell. 53(1), 574\u2013585 (2023)","journal-title":"Appl. Intell."},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., et al.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., et al.: NTU RGB+ D: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"issue":"10","key":"32_CR18","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, M., et al.: NTU RGB+ d 120: a large-scale benchmark for 3D human activity understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2684\u20132701 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR19","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.: Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans. Image Process. 29, 9532\u20139545 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103219","volume":"208","author":"C Plizzari","year":"2021","unstructured":"Plizzari, C., Cannici, M., Matteucci, M.: Skeleton-based action recognition via spatial and temporal transformer networks. Comput. Vis. Image Underst. 208, 103219 (2021)","journal-title":"Comput. Vis. Image Underst."},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., et al.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 143\u2013152","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"32_CR22","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.neucom.2021.02.001","volume":"440","author":"J Xie","year":"2021","unstructured":"Xie, J., Miao, Q., Liu, R., et al.: Attention adjacency matrix based graph convolutional networks for skeleton-based action recognition. Neurocomputing 440, 230\u2013239 (2021)","journal-title":"Neurocomputing"},{"issue":"3","key":"32_CR23","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/s00138-023-01386-2","volume":"34","author":"S Wang","year":"2023","unstructured":"Wang, S., Pan, J., Huang, B., et al.: ICE-GCN: An interactional channel excitation-enhanced graph convolutional network for skeleton-based action recognition. Mach. Vis. Appl. 34(3), 40 (2023)","journal-title":"Mach. Vis. Appl."},{"issue":"14","key":"32_CR24","doi-asserted-by":"publisher","first-page":"17796","DOI":"10.1007\/s10489-022-04442-y","volume":"53","author":"Q Zhu","year":"2023","unstructured":"Zhu, Q., Deng, H.: Spatial adaptive graph convolutional network for skeleton-based action recognition. Appl. Intell. 53(14), 17796\u201317808 (2023)","journal-title":"Appl. Intell."},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Ye, F., Pu, S., Zhong, Q., et al.: Dynamic GCN: context-enriched topology learning for skeleton-based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 55\u201363 (2020)","DOI":"10.1145\/3394171.3413941"},{"key":"32_CR26","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patrec.2021.12.005","volume":"153","author":"Y Zang","year":"2022","unstructured":"Zang, Y., Yang, D., Liu, T., et al.: SparseShift-GCN: High precision skeleton-based action recognition. Pattern Recogn. Lett. 153, 136\u2013143 (2022)","journal-title":"Pattern Recogn. Lett."},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wu, C., Zhang, Z., et al.: ResNest: split-attention networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2736\u20132746 (2022)","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Z., Yuan, C., et al.: 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 (2021)","DOI":"10.1109\/ICCV48922.2021.01311"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5588-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T18:07:23Z","timestamp":1723486043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5588-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755875","9789819755882"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5588-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}