{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:47:08Z","timestamp":1768272428041,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556755","type":"print"},{"value":"9789819556762","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5676-2_16","type":"book-chapter","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T20:32:18Z","timestamp":1768249938000},"page":"233-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fusing Rigid Skeletal Nodes to\u00a0Graph Convolutional Networks for\u00a0Fuzzy Action Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7188-9145","authenticated-orcid":false,"given":"Yuehan","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7500-4979","authenticated-orcid":false,"given":"Hongjun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Yang, X., Tian, Y.L.: Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 14\u201319 (2012)","DOI":"10.1109\/CVPRW.2012.6239232"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Sun, X., Luo, C., Zha, Z.J., Zeng, W.: Spatiotemporal fusion in 3d cnns: A probabilistic view. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9829\u20139838 (2020)","DOI":"10.1109\/CVPR42600.2020.00985"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Wu, Z., Wang, X., Jiang, Y.G., Ye, H., Xue, X.: Modeling spatial-temporal clues in a hybrid deep learning framework for video classification. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 461\u2013470 (2015)","DOI":"10.1145\/2733373.2806222"},{"key":"16_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 (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"16_CR5","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912\u20137921 (2019)","DOI":"10.1109\/CVPR.2019.00810"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chi, H.g., Ha, M.H., Chi, S., Lee, S.W., Huang, Q., Ramani, K.: Infogcn: Representation learning for human skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20186\u201320196 (2022)","DOI":"10.1109\/CVPR52688.2022.01955"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Zhu, X., Huang, Q., Li, C., Cui, J., Chen, Y.: Skeleton-based action recognition with combined part-wise topology graph convolutional networks. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 43\u201359 (2023)","DOI":"10.1007\/978-981-99-8429-9_4"},{"key":"16_CR8","unstructured":"Geng, P., Lu, X., Hu, C., Liu, H., Lyu, L.: Focusing fine-grained action by self-attention-enhanced graph neural networks with contrastive learning. IEEE Trans. Circ. Syst. Video Technol. (2003)"},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.1109\/LSP.2022.3199670","volume":"29","author":"K Liu","year":"2022","unstructured":"Liu, K., Li, Y., Xu, Y., Liu, S., Liu, S.: Spatial focus attention for fine-grained skeleton-based action tasks. IEEE Signal Process. Lett. 29, 1883\u20131887 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., Tang, H.: 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":"16_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, S., Yang, B., Li, Q., Liu, H.: Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 1113\u20131122 (2021)","DOI":"10.1609\/aaai.v35i2.16197"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Zeng, A., Sun, X., Yang, L., Zhao, N., Liu, M., Xu, Q.: Learning skeletal graph neural networks for hard 3d pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11436\u201311445 (2021)","DOI":"10.1109\/ICCV48922.2021.01124"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, Q., Wang, Y.: Learning discriminative representations for skeleton based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10608\u201310617 (2023)","DOI":"10.1109\/CVPR52729.2023.01022"},{"key":"16_CR14","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":"16_CR15","doi-asserted-by":"crossref","unstructured":"Bai, Z., Ding, Q., Tan, J.: Two-steam fully connected graph convolutional network for skeleton-based action recognition. In: Chinese Control and Decision Conference (CCDC), pp. 1056\u20131061 (2020)","DOI":"10.1109\/CCDC49329.2020.9164130"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Z., Yuan, C., Li, B., Deng, Y., Hu, W.: 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"},{"key":"16_CR17","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., Wang, L.: Constructing stronger and faster baselines for skeleton-based action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45, 1474\u20131488 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Xin, W., Miao, Q., Liu, Y., Liu, R., Pun, C.M., Shi, C.: 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":"16_CR19","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":"16_CR20","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., Wang, G., Duan, L.Y., Kot, A.C.: Ntu rgb+ d 120: A large-scale benchmark for 3D human activity understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2684\u20132701 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Wang, J., Nie, X., Xia, Y., Wu, Y., Zhu, S.C.: Cross-view action modeling, learning and recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2649\u20132656 (2014)","DOI":"10.1109\/CVPR.2014.339"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Song, Y.F., Zhang, Z., Wang, L.: Richly activated graph convolutional network for action recognition with incomplete skeletons. In: IEEE International Conference on Image Processing (ICIP). vol.\u00a036, pp.\u00a01\u20135 (2019)","DOI":"10.1109\/ICIP.2019.8802917"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: 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":"16_CR24","doi-asserted-by":"crossref","unstructured":"Huang, L., Huang, Y., Ouyang, W., Wang, L.: Part-level graph convolutional network for skeleton-based action recognition. In: Neuromuscular Junction. Handbook of experimental pharmacology, vol.\u00a034, pp. 11045\u201311052 (2020)","DOI":"10.1609\/aaai.v34i07.6759"},{"key":"16_CR25","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 183\u2013192 (2020)","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: 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. 143\u2013152 (2020)","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Ke, L., Peng, K.C., Lyu, S.: Towards to-at spatio-temporal focus for skeleton-based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 1131\u20131139 (2022)","DOI":"10.1609\/aaai.v36i1.19998"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, Y., Chen, Y., Yuan, C., Li, B., Hu, W.: Transkeleton: Hierarchical spatial-temporal transformer for skeleton-based action recognition. IEEE Trans. Circ. Syst. Video Technol. (2023)","DOI":"10.1109\/TCSVT.2023.3240472"},{"key":"16_CR29","unstructured":"Zhou, Y., Cheng, Z.Q., He, J.Y., Luo, B., Geng, Y., Xie, X.: Overcoming topology agnosticism: Enhancing skeleton-based action recognition through redefined skeletal topology awareness (2023). https:\/\/arxiv.org\/abs\/2305.11468"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Yun, X., et al.: Behavioral recognition of skeletal data based on targeted dual fusion strategy. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 6917\u20136925 (2024)","DOI":"10.1609\/aaai.v38i7.28517"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Jang, S., Lee, H., Kim, W.J., Lee, J., Woo, S., Lee, S.: Multi-scale structural graph convolutional network for skeleton-based action recognition. IEEE Trans. Circ. Syst. Video Technol. (2024)","DOI":"10.1109\/TCSVT.2024.3375512"}],"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\/978-981-95-5676-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T20:32:24Z","timestamp":1768249944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5676-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556755","9789819556762"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5676-2_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"13 January 2026","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":"Shanghai","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}