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Two-stream 3D convolutional neural network for skeleton-based action recognition. 2017. doi:10.48550\/arXiv.1705.08106."},{"key":"ref7","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1010","article-title":"NTU RGB+D: a large scale dataset for 3D human activity analysis","author":"Shahroudy","year":"2016"},{"key":"ref8","series-title":"Proceedings of the IEEE International Conference On Computer Vision","first-page":"2117","article-title":"View adaptive recurrent neural networks for high performance human action recognition from skeleton data","author":"Zhang","year":"2017"},{"key":"ref9","series-title":"2019 IEEE International Conference on Multimedia and Expo (ICME)","first-page":"826","article-title":"Relational network for skeleton-based action recognition","author":"Zheng","year":"2019"},{"key":"ref10","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"32","author":"Yan","year":"2018","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref11","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1227","article-title":"An attention enhanced graph convolutional LSTM network for skeleton-based action recognition","author":"Si","year":"2019"},{"key":"ref12","first-page":"8989","article-title":"Graph CNNs with motif and variable temporal block for skeleton-based action recognition","volume":"33","author":"Wen","year":"2019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref13","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12026","article-title":"Two-stream adaptive graph convolutional networks for skeleton-based action recognition","author":"Shi","year":"2019"},{"key":"ref14","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1112","article-title":"Semantics-guided neural networks for efficient skeleton-based human action recognition","author":"Zhang","year":"2020"},{"key":"ref15","series-title":"Proceedings of the 28th ACM International Conference on Multimedia","first-page":"55","article-title":"Dynamic GCN: context-enriched topology learning for skeleton-based action recognition","author":"Ye","year":"2020"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"107805","DOI":"10.1016\/j.compeleceng.2022.107805","article-title":"HaredNet: a deep learning based architecture for autonomous video surveillance by recognizing human actions","volume":"99","author":"Nasir","year":"2022","journal-title":"Comput Electr Eng"},{"key":"ref17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10 631","article-title":"IMiGUE: an identity-free video dataset for micro-gesture understanding and emotion analysis","author":"Liu","year":"2021"},{"key":"ref18","unstructured":"Kay W, Carreira J, Simonyan K, Zhang B, Hillier C, Vijayanarasimhan S, et al. The kinetics human action video dataset. 2017. doi:10.48550\/arXiv.1705.06950."},{"key":"ref19","series-title":"International Conference on Machine Learning","first-page":"2014","article-title":"Learning convolutional neural networks for graphs","author":"Niepert","year":"2016"},{"key":"ref20","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"5115","article-title":"Geometric deep learning on graphs and manifolds using mixture model CNNs","author":"Monti","year":"2017"},{"key":"ref21","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref22","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y. Spectral networks and locally connected networks on graphs. 2013. doi:10.48550\/arXiv.1312.6203."},{"key":"ref23","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"28","author":"Duvenaud","year":"2015","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref24","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref25","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. 2016. doi:10.48550\/arXiv.1609.02907."},{"key":"ref26","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"Statistics"},{"key":"ref27","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"1416","article-title":"Large-scale learnable graph convolutional networks","author":"Gao","year":"2018"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"36 475","DOI":"10.1109\/ACCESS.2020.3049029","article-title":"Multi-scale mixed dense graph convolution network for skeleton-based action recognition","volume":"9","author":"Xia","year":"2021","journal-title":"IEEE Access"},{"key":"ref29","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6882","article-title":"Bayesian graph convolution LSTM for skeleton based action recognition","author":"Zhao","year":"2019"},{"key":"ref30","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"5323","article-title":"Deep progressive reinforcement learning for skeleton-based action recognition","author":"Tang","year":"2018"},{"key":"ref31","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"183","article-title":"Skeleton-based action recognition with shift graph convolutional network","author":"Cheng","year":"2020"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/LRA.2021.3056361","article-title":"Pose refinement graph convolutional network for skeleton-based action recognition","volume":"6","author":"Li","year":"2021","journal-title":"IEEE Robot Autom Lett"},{"key":"ref33","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"13 359","article-title":"Channel-wise topology refinement graph convolution for skeleton-based action recognition","author":"Chen","year":"2021"},{"key":"ref34","series-title":"Computer Vision-ECCV 2020: 16th European Conference","first-page":"536","article-title":"Decoupling GCN with dropgraph module for skeleton-based action recognition","author":"Cheng","year":"2020"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"8699","DOI":"10.1109\/TMM.2023.3239751","article-title":"Skeleton-based action recognition through contrasting two-stream spatial-temporal networks","volume":"25","author":"Pang","year":"2023","journal-title":"IEEE Trans Multimedia"},{"key":"ref36","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10444","article-title":"Hierarchically decomposed graph convolutional networks for skeleton-based action recognition","author":"Lee","year":"2023"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s00138-023-01386-2","article-title":"ICE-GCN: an interactional channel excitation-enhanced graph convolutional network for skeleton-based action recognition","volume":"34","author":"Wang","year":"2023","journal-title":"Mach Vision Appl"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TVCG.2023.3247075","article-title":"Skeleton-based human action recognition via large-kernel attention graph convolutional network","volume":"29","author":"Liu","year":"2023","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"ref39","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7291","article-title":"Realtime multi-person 2D pose estimation using part affinity fields","author":"Cao","year":"2017"},{"key":"ref40","series-title":"Proceedings of the British Machine Vision Conference 2018 (BMVC)","article-title":"Bidirectional long short-term memory variational autoencoder","author":"Shi","year":"2018"},{"key":"ref41","first-page":"2669","article-title":"Learning graph convolutional network for skeleton-based human action recognition by neural searching","volume":"34","author":"Peng","year":"2020","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref42","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"143","article-title":"Disentangling and unifying graph convolutions for skeleton-based action recognition","author":"Liu","year":"2020"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Duan H, Wang J, Chen K, Lin D. 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