Computer Science ›› 2022, Vol. 49 ›› Issue (2): 62-68.doi: 10.11896/jsjkx.210900059
• Computer Vision: Theory and Application • Previous Articles Next Articles
XIE Yu1, YANG Rui-ling1, LIU Gong-xu2, LI De-yu1, WANG Wen-jian1
CLC Number:
[1]CHEN Y,TIAN Y,HE M.Monocular Human Pose Estimation:A Survey of Deep Learning-Based Methods[J].Computer Vision and Image Understanding,2020,192:102897. [2]SONG S,LAN C,XING J,et al.An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data[C]//AAAI Conference on Artificial Intelligence.2017:4263-4270. [3]YAN S,XIONG Y,LIN D.Spatial Temporal Graph Convolu-tional Networks for Skeleton-Based Action Recognition[C]//AAAI Conference on Artificial Intelligence.2018:7444-7452. [4]XIONG X,MIN W,ZHENG W S,et al.S3D-CNN:Skeleton-Based 3D Consecutive-Low-Pooling Neural Network for Fall Detection[J].Applied Intelligence,2020,50(10):3521-3534. [5]SHI L,ZHANG Y,CHENG J,et al.Two-Stream AdaptiveGraph Convolutional Networks for Skeleton-Based Action Re-cognition[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:12026-12035. [6]ZHANG P,LAN C,ZENG W,et al.Semantics-Guided NeuralNetworks for Efficient Skeleton-Based Human Action Recognition[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1112-1121. [7]DING C Y,LIU K,LI G,et al.Spatio-Temporal Weighted Posture Motion Features for Human Skeleton Action Recognition Research[J].Chinese Journal of Computers,2020,43(1):29-40. [8]TIAN Z Q,DENG C H,ZHANG J W.Human Behavior Recognition Algorithm Based on Skeletal Temporal Divergence Feature[J].Journal of Computer Applications,2021,41(5):1450-1457. [9]SHI L,ZHANG Y,CHENG J,et al.Skeleton-based Action Re-cognition with Directed Graph Neural Networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019:7912-7921. [10]TANG Y,TIAN Y,LU J,et al.Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018:5323-5332. [11]THAKKAR K,NARAYANAN P J.Part-Based Graph Convolutional Network for Action Recognition[J].arXiv:1809.04983,2018. [12]LI M,CHEN S,CHEN X,et al.Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction[J/OL].IEEE Transactions on Pattern Ana-lysis and Machine Intelligence.https://ieeexplore.ieee.org/document/9334430. [13]LI B,LI X,ZHANG Z,et al.Spatio-temporal Graph Routing for Skeleton-based Action Recognition[C]//AAAI Conference on Artificial Intelligence.2019:8561-8568. [14]HADSELL R,RAO D,RUSU A A,et al.Embracing Change:Continual Learning in Deep Neural Networks[J].Trends in Cognitive Sciences,2020,24(12):1028-1040. [15]CHEN P H,WEI W,HSIEH C,et al.Overcoming Catastrophic Forgetting by Generative Regularization[J].arXiv:1912.01238,2019. [16]D'AUTUME C D M,RUDER S,KONG L,et al.EpisodicMemory in Lifelong Language Learning[J].Advances in Neural Information Processing Systems,2019,32:13143-13152. [17]ROLNICK D, AHUJA A, SCHWARZ J,et al.Experience Replay for Continual Learning[J].Advances in Neural Information Processing Systems,2019,32:350-360. [18]LIU L,PU H Y.Real-time LSTM-based Multi-dimensional Features Gesture Recognition[J].Computer Science,2021,48(8):328-333. [19]KOU X,LIN Y,LIU S,et al.Disentangle-based ContinualGraph Representation Learning[C]//Conference on Empirical Methods in Natural Language Processing.2020:2961-2972. [20]PENG W,HONG X,CHEN H,et al.Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching[C]//AAAI Conference on Artificial Intelligence.2020:2669-2676. [21]PLIZZARI C,CANNICI M,MATTEUCCI M.Skeleton-basedAction Recognition Via Spatial and Temporal Transformer Networks[J].Computer Vision and Image Understanding,2021,208:103219. |
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