{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:42:09Z","timestamp":1743057729815,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981403"},{"type":"electronic","value":"9789819981410"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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\/978-981-99-8141-0_13","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"162-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Supervised Spatio-Temporal Contrastive Learning Framework with\u00a0Optimal Skeleton Subgraph Topology for\u00a0Human Action Recognition"],"prefix":"10.1007","author":[{"given":"Zelin","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Gajjar, V., Gurnani, A., Khandhediya, Y.: Human detection and tracking for video surveillance: a cognitive science approach. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2805\u20132809 (2017)","DOI":"10.1109\/ICCVW.2017.330"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.concog.2018.11.008","volume":"67","author":"A Saha\u00ef","year":"2019","unstructured":"Saha\u00ef, A., Desantis, A., Grynszpan, O., Pacherie, E., Berberian, B.: Action co-representation and the sense of agency during a joint simon task: comparing human and machine co-agents. Conscious. Cogn. 67, 44\u201355 (2019)","journal-title":"Conscious. Cogn."},{"key":"13_CR3","unstructured":"Pilarski, P.M., Butcher, A., Johanson, M., Botvinick, M.M., Bolt, A., Parker, A.S.: Learned human-agent decision-making, communication and joint action in a virtual reality environment. arXiv preprint arXiv:1905.02691 (2019)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551\u20133558 (2013)","DOI":"10.1109\/ICCV.2013.441"},{"key":"13_CR5","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"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Kim, T.S., Reiter, A.: Interpretable 3D human action analysis with temporal convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20\u201328 (2017)","DOI":"10.1109\/CVPRW.2017.207"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Duan, H., Zhao, Y., Chen, K., Lin,D., Dai, B.: Revisiting skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2969\u20132978 (2022)","DOI":"10.1109\/CVPR52688.2022.00298"},{"key":"13_CR9","unstructured":"Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R.: Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080 (2014)"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2117\u20132126 (2017)","DOI":"10.1109\/ICCV.2017.233"},{"key":"13_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107040","volume":"227","author":"X Ji","year":"2021","unstructured":"Ji, X., Zhao, Q., Cheng, J., Ma, C.: Exploiting spatio-temporal representation for 3D human action recognition from depth map sequences. Knowl.-Based Syst. 227, 107040 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: 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":"13_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.: 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 (2020)","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107921","volume":"115","author":"W Peng","year":"2021","unstructured":"Peng, W., Hong, X., Zhao, G.: Tripool: graph triplet pooling for 3d skeleton-based action recognition. Pattern Recogn. 115, 107921 (2021)","journal-title":"Pattern Recogn."},{"key":"13_CR16","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 297\u2013304. JMLR Workshop and Conference Proceedings (2010)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"13_CR18","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR19","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"13_CR20","unstructured":"Hinton, G., Vinyals, O., Dean, J., et\u00a0al.: Distilling the knowledge in a neural network, vol. 2, no. 7. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: 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 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"13_CR23","unstructured":"Kay, W., et\u00a0al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2021.04.023","volume":"569","author":"H Rao","year":"2021","unstructured":"Rao, H., Shihao, X., Xiping, H., Cheng, J., Bin, H.: Augmented skeleton based contrastive action learning with momentum LSTM for unsupervised action recognition. Inf. Sci. 569, 90\u2013109 (2021)","journal-title":"Inf. Sci."},{"issue":"5","key":"13_CR25","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.3390\/s23052452","volume":"23","author":"C Dai","year":"2023","unstructured":"Dai, C., Wei, Y., Xu, Z., Chen, M., Liu, Y., Fan, J.: ConMLP: MLP-based self-supervised contrastive learning for skeleton data analysis and action recognition. Sensors 23(5), 2452 (2023)","journal-title":"Sensors"},{"issue":"9","key":"13_CR26","doi-asserted-by":"publisher","first-page":"4800","DOI":"10.1109\/TNNLS.2021.3061115","volume":"33","author":"C Li","year":"2021","unstructured":"Li, C., Xie, C., Zhang, B., Han, J., Zhen, X., Chen, J.: Memory attention networks for skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4800\u20134814 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"13_CR27","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/LRA.2021.3056361","volume":"6","author":"S Li","year":"2021","unstructured":"Li, S., Yi, J., Farha, Y.A., Gall, J.: Pose refinement graph convolutional network for skeleton-based action recognition. IEEE Rob. Autom. Lett. 6(2), 1028\u20131035 (2021)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"13_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2019.115776","volume":"83","author":"W Ding","year":"2020","unstructured":"Ding, W., Li, X., Li, G., Wei, Y.: Global relational reasoning with spatial temporal graph interaction networks for skeleton-based action recognition. Signal Process. Image Commun. 83, 115776 (2020)","journal-title":"Signal Process. Image Commun."},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: 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 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Gao, X., Hu, W., Tang, J., Liu,J., Guo, Z.: Optimized skeleton-based action recognition via sparsified graph regression. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 601\u2013610 (2019)","DOI":"10.1145\/3343031.3351170"},{"key":"13_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108146","volume":"240","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Zhang, H., Dan, X., He, K.: Graph transformer network with temporal kernel attention for skeleton-based action recognition. Knowl.-Based Syst. 240, 108146 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Li, B., Li, X., Zhang, Z., Fei, W.: Spatio-temporal graph routing for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8561\u20138568 (2019)","DOI":"10.1609\/aaai.v33i01.33018561"},{"issue":"3","key":"13_CR33","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1007\/s10489-021-02487-z","volume":"52","author":"Y Yoon","year":"2022","unstructured":"Yoon, Y., Jongmin, Yu., Jeon, M.: Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition. Appl. Intell. 52(3), 2317\u20132331 (2022)","journal-title":"Appl. Intell."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8141-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:36:49Z","timestamp":1710344209000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8141-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981403","9789819981410"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8141-0_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}