{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:10:33Z","timestamp":1742919033835,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819785100"},{"type":"electronic","value":"9789819785117"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-8511-7_36","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:10:39Z","timestamp":1730524239000},"page":"511-524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-scale Spatial and Temporal Feature Aggregation Graph Convolutional Network for Skeleton-Based Action Recognition"],"prefix":"10.1007","author":[{"given":"Yifei","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"issue":"6","key":"36_CR1","first-page":"84","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Neural Inf. Process. Syst. 60(6), 84\u201390 (2012)","journal-title":"Neural Inf. Process. Syst."},{"issue":"10","key":"36_CR2","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., et al.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222\u20132232 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"36_CR3","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.1007\/s11063-020-10404-7","volume":"54","author":"H Zhang","year":"2022","unstructured":"Zhang, H., Lu, G., Zhan, M., et al.: Semi-supervised classification of graph convolutional networks with Laplacian rank constraints. Neural Process. Lett. 54(4), 2645\u20132656 (2022)","journal-title":"Neural Process. Lett."},{"key":"36_CR4","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":"36_CR5","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":"36_CR6","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":"36_CR7","doi-asserted-by":"crossref","unstructured":"Peng, W., Hong, X., Chen, H., et al.: Learning graph convolutional network for skeleton-based human action recognition by neural searching. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2669\u20132676 (2020)","DOI":"10.1609\/aaai.v34i03.5652"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, L., Peng, X., Tian, Y., et al.: Semantic graph convolutional networks for 3D human pose regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3425\u20133435 (2019)","DOI":"10.1109\/CVPR.2019.00354"},{"key":"36_CR9","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":"36_CR10","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":"36_CR11","doi-asserted-by":"crossref","unstructured":"Wang, J., Nie, X., Xia, Y., et al.: Cross-view action modeling, learning and recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2649\u20132656 (2014)","DOI":"10.1109\/CVPR.2014.339"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Du, Y., Fu, Y., Wang, L.: Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian Conference on Pattern Recognition, pp. 579\u2013583 (2015)","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Li, B., Dai, Y., Cheng, X., et al.: Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN. In: 2017 IEEE International Conference on Multimedia & Expo Workshops, pp. 601\u2013604 (2017)","DOI":"10.1109\/ICMEW.2017.8026282"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Xing, J., et al.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2117\u20132126 (2017)","DOI":"10.1109\/ICCV.2017.233"},{"issue":"9","key":"36_CR15","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., et al.: Fusing geometric features for skeleton-based action recognition using multilayer LSTM networks. IEEE Trans. Multimed. 20(9), 2330\u20132343 (2018)","journal-title":"IEEE Trans. Multimed."},{"key":"36_CR16","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":"36_CR17","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zhang, Y., Cao, C., et al.: Decoupling GCN with DropGraph module for skeleton-based action recognition. In: Proceedings of the European Conference on Computer Vision, pp. 536\u2013553 (2020)","DOI":"10.1007\/978-3-030-58586-0_32"},{"key":"36_CR18","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"},{"issue":"5","key":"36_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., et al.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"36_CR20","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, pp. 140\u2013149 (2020)","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Xin, W., Miao, Q., Liu, Y., et al.: Skeleton MixFormer: Multivariate topology representation for skeleton-based action recognition. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 2211\u20132220 (2023)","DOI":"10.1145\/3581783.3611900"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zhang, Y., He, X., et al.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 180\u2013189 (2020)","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, P., Lan, C., Zeng, W., et al.: 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. 1109\u20131118 (2020)","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., et al.: An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227\u20131236 (2019)","DOI":"10.1109\/CVPR.2019.00132"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Xu, K., Ye, F., Zhong, Q., et al.: Topology-aware convolutional neural network for efficient skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2866\u20132874 (2022)","DOI":"10.1609\/aaai.v36i3.20191"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., et al.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3590\u20133598 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, S., Yang, B., et al.: Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1113\u20131122 (2021)","DOI":"10.1609\/aaai.v35i2.16197"},{"issue":"2","key":"36_CR30","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."},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Pang, C., Gao, X., Chen, Z., et al.: Self-adaptive graph with nonlocal attention network for skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3298950"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, X., Huang, Q., Li, C., et al.: Skeleton-based action recognition with combined part-wise topology graph convolutional networks. In: Proceedings of the Pattern Recognition and Computer Vision, pp. 43\u201359 (2023)","DOI":"10.1007\/978-981-99-8429-9_4"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8511-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T05:15:36Z","timestamp":1730524536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8511-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819785100","9789819785117"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8511-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}