{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:43:14Z","timestamp":1758818594997,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030870935"},{"type":"electronic","value":"9783030870942"}],"license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-87094-2_17","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T07:06:45Z","timestamp":1637132805000},"page":"191-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Asymmetric Convolution View Adaptation Networks for Skeleton-Based Human Action Recognition"],"prefix":"10.1007","author":[{"given":"Tianyu","family":"Ma","sequence":"first","affiliation":[]},{"given":"Jiahui","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hongwei","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Zhaojie","family":"Ju","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Pham, D.-T., Nguyen, T.-N., Le, T.-L.H., Vu.: Spatio-temporal representation for skeleton-based human action recognition. In: 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), October 2020","DOI":"10.1109\/MAPR49794.2020.9237766"},{"key":"17_CR2","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: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.01230"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7912\u20137921 (2019)","DOI":"10.1109\/CVPR.2019.00810"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Li, C., Zhong, Q., Xie, D., Pu, S.: Cooccurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, July 2018","DOI":"10.24963\/ijcai.2018\/109"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: CVPR, pp. 9\u201314 (2010)","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Yang, X., Tian, Y.: Eigen joints-based action recognition using Naive-Bayes-nearest-neighbor. In: CVPR, pp. 14\u201319 (2012)","DOI":"10.1109\/CVPRW.2012.6239232"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Oreifej, O., Liu, Z.: HON4D: histogram of oriented 4D normals for activity recognition from depth sequences. In: CVPR, pp. 716\u2013723 (2013)","DOI":"10.1109\/CVPR.2013.98"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Rahmani, H., Mian, A.: 3D action recognition from novel viewpoints. In: CVPR, p. 12 (2016)","DOI":"10.1109\/CVPR.2016.167"},{"key":"17_CR9","unstructured":"Wang, P., Li, W., Gao, Z., Zhang, J., Tang, C., Ogunbona, P.: Deep convolutional neural networks for action recognition using depth map sequences. In: CVPR, pp. 1\u20138 (2015)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297\u20131304 (2011)","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Pham, D.-T., Nguyen, T.-N., Le, T.-L., Vu, H.: Analyzing role of joint subset selection in human action recognition. In: 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), December 2019","DOI":"10.1109\/NICS48868.2019.9023859"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"43243","DOI":"10.1109\/ACCESS.2020.2977856","volume":"8","author":"J Yu","year":"2020","unstructured":"Yu, J., Gao, H., Yang, W., Chin, W., Kubota, N., Ju, Z.: A discriminative deep model with feature fusion and temporal attention for human action recognition. IEEE Access 8, 43243\u201343255 (2020)","journal-title":"IEEE Access"},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1007\/978-3-030-58586-0_32","volume-title":"Computer Vision \u2013 ECCV 2020","author":"K Cheng","year":"2020","unstructured":"Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., Lu, H.: Decoupling GCN with DropGraph module for skeleton-based action recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 536\u2013553. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_32"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Ding, G., Han, J., ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. IEEE Int. Conf. Comput. Vis. (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00200"},{"issue":"8","key":"17_CR15","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., Zeng, W., Xue, J., Zheng, N.: 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."},{"key":"17_CR16","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: CVPR (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"17_CR17","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. IEEE Int. Conf. Comput. Vis. 2117\u20132126 (2017)","DOI":"10.1109\/ICCV.2017.233"},{"key":"17_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-01246-5_7","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Si","year":"2018","unstructured":"Si, C., Jing, Y., Wang, W., Wang, L., Tan, T.: Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 106\u2013121. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_7"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Li, T., Fan, L., Zhao, M., Liu, Y, Katabi, D.: Making the invisible visible: action recognition through walls and occlusions. IEEE Int. Conf. Comput. Vis. 872\u2013881 (2019)","DOI":"10.1109\/ICCV.2019.00096"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. AAAI Conf. Artif. Intell. (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, L., Guan, L., Liu, M.: GFNet: a lightweight group frame network for efficient human action recognition. IEEE Int. Conf. Acoust. Speech Sig. Process. 2583\u20132587 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053939"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Fan, Y., Weng, S., Zhang, Y., Shi, B., Zhang, Y.: Context-aware cross-attention for skeleton-based human action recognition. IEEE Trans. Image Process. Database: Compendex, 15280\u201315290 (2020)","DOI":"10.1109\/ACCESS.2020.2968054"},{"issue":"2","key":"17_CR23","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 Robot. Autom. Lett. 6(2), 1028\u20131035 (2021)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00026"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87094-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T12:38:41Z","timestamp":1673786321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87094-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"ISBN":["9783030870935","9783030870942"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87094-2_17","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2021,11,18]]},"assertion":[{"value":"18 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UKCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"UK Workshop on Computational Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberystwyth","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2021","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":"ukci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ukci2021.dcs.aber.ac.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}