{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T20:03:23Z","timestamp":1772309003717,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council (CSC)","doi-asserted-by":"publisher","award":["csc201903170208"],"award-info":[{"award-number":["csc201903170208"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council (CSC)","doi-asserted-by":"publisher","award":["RGPIN-2020-05095"],"award-info":[{"award-number":["RGPIN-2020-05095"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","award":["csc201903170208"],"award-info":[{"award-number":["csc201903170208"]}]},{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)","award":["RGPIN-2020-05095"],"award-info":[{"award-number":["RGPIN-2020-05095"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Recognizing human actions can help in numerous ways, such as health monitoring, intelligent surveillance, virtual reality and human\u2013computer interaction. A quick and accurate detection algorithm is required for daily real-time detection. This paper first proposes to generate a lightweight graph neural network by self-distillation for human action recognition tasks. The lightweight graph neural network was evaluated on the NTU-RGB+D dataset. The results demonstrate that, with competitive accuracy, the heavyweight graph neural network can be compressed by up to 80%. Furthermore, the learned representations have denser clusters, estimated by the Davies\u2013Bouldin index, the Dunn index and silhouette coefficients. The ideal input data and algorithm capacity are also discussed.<\/jats:p>","DOI":"10.3390\/a16120552","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T04:12:56Z","timestamp":1701403976000},"page":"552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1693-2286","authenticated-orcid":false,"given":"Miao","family":"Feng","sequence":"first","affiliation":[{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, QC H3C 3J7, Canada"}]},{"given":"Jean","family":"Meunier","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Operations Research, University of Montreal, Montreal, QC H3C 3J7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., and Lin, D. (2018, January 2\u20137). Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LO, USA.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., and Lu, H. (2019, January 15\u201320). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref_3","unstructured":"Cheng, Y., Wang, D., Zhou, P., and Zhang, T. (2020). A Survey of Model Compression and Acceleration for Deep Neural Networks. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., and Ma, K. (2019). Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation. arXiv.","DOI":"10.1109\/ICCV.2019.00381"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Veli\u010dkovi\u0107, P. (2023). Everything is connected: Graph neural networks. Curr. Opin. Struct. Biol., 79.","DOI":"10.1016\/j.sbi.2023.102538"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/TSC.2022.3196915","article-title":"A Graph Neural Network and Pointer Network-Based Approach for QoS-Aware Service Composition","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hu, Y., Han, N., Yang, A., Liu, X., and Cai, H. (2023). A survey of drug-target interaction and affinity prediction methods via graph neural networks. Comput. Biol. Med., 163.","DOI":"10.1016\/j.compbiomed.2023.107136"},{"key":"ref_9","first-page":"101235","article-title":"Utilizing citation network structure to predict paper citation counts: A Deep learning approach","volume":"16","author":"Zhao","year":"2022","journal-title":"J. Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"061815","DOI":"10.1117\/1.JEI.31.6.061815","article-title":"Carrot grading system using computer vision feature parameters and a cascaded graph convolutional neural network","volume":"31","author":"Bukumira","year":"2022","journal-title":"J. Electron. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jmsy.2023.06.005","article-title":"Graph neural networks-based scheduler for production planning problems using reinforcement learning","volume":"69","author":"Hameed","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_12","first-page":"1","article-title":"Graph representation learning","volume":"14","author":"Hamilton","year":"2020","journal-title":"Synth. Lect. Artifical Intell. Mach. Learn."},{"key":"ref_13","unstructured":"Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/IJCNN.2005.1555942","article-title":"A new model for learning in graph domains","volume":"Volume 2","author":"Gori","year":"2005","journal-title":"Proceedings of the 2005 IEEE International Joint Conference on Neural Networks"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, M., and Meunier, J. (2022). Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey. Sensors, 22.","DOI":"10.3390\/s22062091"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, H., and Meng, L. (2023). Model Compression for Deep Neural Networks: A Survey. Computers, 12.","DOI":"10.3390\/computers12030060"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.T., and Wang, G. (2016, January 27\u201330). Ntu rgb+ d: A large scale dataset for 3d human activity analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.115"},{"key":"ref_19","first-page":"6906","article-title":"Does knowledge distillation really work?","volume":"34","author":"Stanton","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A Cluster Separation Measure","volume":"PAMI-1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","article-title":"A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters","volume":"3","author":"Dunn","year":"1973","journal-title":"J. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01969727408546059","article-title":"Well-Separated Clusters and Optimal Fuzzy Partitions","volume":"4","year":"1974","journal-title":"J. Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_25","first-page":"833","article-title":"Stochastic neighbor embedding","volume":"15","author":"Hinton","year":"2002","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","unstructured":"Preparata, F.P., and Shamos, M.I. (2012). Computational Geometry: An Introduction, Springer Science & Business Media."},{"key":"ref_28","unstructured":"Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., and Natsev, P. (2017). The kinetics human action video dataset. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/12\/552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:35:56Z","timestamp":1760132156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/12\/552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":28,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["a16120552"],"URL":"https:\/\/doi.org\/10.3390\/a16120552","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,1]]}}}