17.1.3.6.18 Human Action Recognition, Neural Nets for Skeletal Representations

Chapter Contents (Back)
Action Recognition. Action Detection. Neural Network. Skeletal Action Recognition. Human Actions. Relate to:
See also Human Action Recognition and Detection Using Human Pose. And:
See also Human Action Recognition, Part Models, Human Pose.
See also Articulatd Action Recognition.

Du, Y.[Yong], Fu, Y., Wang, L.[Liang],
Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition,
IP(25), No. 7, July 2016, pp. 3010-3022.
IEEE DOI 1606
bone BibRef

Si, C.Y.[Chen-Yang], Jing, Y.[Ya], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network,
PR(107), 2020, pp. 107511.
Elsevier DOI 2008
BibRef
Earlier:
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning,
ECCV18(I: 106-121).
Springer DOI 1810
Skeleton-based action recognition, Hierarchical spatial reasoning, Temporal stack learning, Clip-based incremental loss BibRef

Si, C.Y.[Chen-Yang], Chen, W.T.[Wen-Tao], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition,
CVPR19(1227-1236).
IEEE DOI 2002
BibRef

Song, Y.F.[Yi-Fan], Zhang, Z.[Zhang], Shan, C.F.[Cai-Feng], Wang, L.[Liang],
Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition,
CirSysVideo(31), No. 5, 2021, pp. 1915-1925.
IEEE DOI 2105
BibRef
Earlier: A1, A2, A4, Only:
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons,
ICIP19(1-5)
IEEE DOI 1910
Action Recognition, Skeleton Data, Graph Convolutional Network, Activation Maps, Occlusion BibRef

Jing, Y.[Ya], Wang, J.[Junbo], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Relational graph neural network for situation recognition,
PR(108), 2020, pp. 107544.
Elsevier DOI 2008
Situation recognition, Relationship modeling, Graph neural network, Reinforcement learning BibRef

Du, Y.[Yong], Wang, W.[Wei], Wang, L.[Liang],
Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition,
CVPR15(1110-1118)
IEEE DOI 1510
BibRef

Wang, H.S.[Hong-Song], Wang, L.[Liang],
Beyond Joints: Learning Representations From Primitive Geometries for Skeleton-Based Action Recognition and Detection,
IP(27), No. 9, September 2018, pp. 4382-4394.
IEEE DOI 1807
feature extraction, image classification, image motion analysis, image representation, learning (artificial intelligence), viewpoint transformation BibRef

Wang, P.[Peng], Wen, J.[Jun], Si, C.Y.[Chen-Yang], Qian, Y.T.[Yun-Tao], Wang, L.[Liang],
Contrast-Reconstruction Representation Learning for Self-Supervised Skeleton-Based Action Recognition,
IP(31), 2022, pp. 6224-6238.
IEEE DOI 2210
Skeleton, Dynamics, Representation learning, Image reconstruction, Task analysis, Computational modeling, Visualization, contrastive learning BibRef

Hu, L.Z.[Li-Zhang], Xu, J.H.[Jin-Hua],
Learning Discriminative Representation for Skeletal Action Recognition Using LSTM Networks,
CAIP17(II: 94-104).
Springer DOI 1708
BibRef

Li, C.L.[Chao-Long], Cui, Z.[Zhen], Zheng, W.M.[Wen-Ming], Xu, C.Y.[Chun-Yan], Ji, R.R.[Rong-Rong], Yang, J.[Jian],
Action-Attending Graphic Neural Network,
IP(27), No. 7, July 2018, pp. 3657-3670.
IEEE DOI 1805
Dynamics, Feature extraction, Hidden Markov models, Joints, Neural networks, skeleton-based action recognition BibRef

Pham, H.H.[Huy-Hieu], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
Exploiting deep residual networks for human action recognition from skeletal data,
CVIU(170), 2018, pp. 51-66.
Elsevier DOI 1806
3D Action recognition, Deep residual networks, Skeletal data BibRef

Pham, H.H.[Huy Hieu], Salmane, H.[Houssam], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data,
ICIAR19(I:18-32).
Springer DOI 1909
BibRef

Xu, Y.Y.[Yang-Yang], Cheng, J.[Jun], Wang, L.[Lei], Xia, H.Y.[Hai-Ying], Liu, F.[Feng], Tao, D.P.[Da-Peng],
Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition,
SPLetters(25), No. 7, July 2018, pp. 1044-1048.
IEEE DOI 1807
bone, convolution, feature extraction, image motion analysis, image recognition, learning (artificial intelligence), skeleton BibRef

Wang, L.[Lei], Zhang, J.W.[Jian-Wei], Yang, S.[Shanmin], Gu, S.[Song],
Two-stream spatiotemporal networks for skeleton action recognition,
IET-IPR(17), No. 11, 2023, pp. 3358-3370.
DOI Link 2310
character recognition, graph theory, image recognition, neural nets, pattern recognition, video surveillance BibRef

Zhang, S.Y.[Song-Yang], Yang, Y.[Yang], Xiao, J.[Jun], Liu, X.M.[Xiao-Ming], Yang, Y.[Yi], Xie, D.[Di], Zhuang, Y.T.[Yue-Ting],
Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
MultMed(20), No. 9, September 2018, pp. 2330-2343.
IEEE DOI 1809
BibRef
Earlier: A1, A4, A3, Only:
On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks,
WACV17(148-157)
IEEE DOI 1609
feature extraction, image recognition, optimisation, recurrent neural nets, recurrent neural network models, score fusion. Computational modeling, Logic gates, Neurons, Nonhomogeneous media, Skeleton. BibRef

Pham, H.H.[Huy-Hieu], Khoudour, L.[Louahdi], Crouzil, A.[Alain], Zegers, P.[Pablo], Velastin, S.A.[Sergio A.],
Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks,
IET-CV(13), No. 3, April 2019, pp. 319-328.
DOI Link 1904
BibRef
Earlier:
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks,
ICIP18(3483-3487)
IEEE DOI 1809
Skeleton, Image color analysis, Training, Task analysis, Hidden Markov models, Feature extraction, CNNs BibRef

Zhang, P.F.[Peng-Fei], Lan, C.L.[Cui-Ling], Xing, J.L.[Jun-Liang], Zeng, W.J.[Wen-Jun], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition,
PAMI(41), No. 8, August 2019, pp. 1963-1978.
IEEE DOI 1907
Skeleton, Adaptation models, Adaptive systems, Recurrent neural networks, Cameras, consistent BibRef

Zhang, P.F.[Peng-Fei], Xue, J.R.[Jian-Ru], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun], Gao, Z.N.[Zhan-Ning], Zheng, N.N.[Nan-Ning],
Adding Attentiveness to the Neurons in Recurrent Neural Networks,
ECCV18(IX: 136-152).
Springer DOI 1810
BibRef

Zhang, P.F.[Peng-Fei], Lan, C.L.[Cui-Ling], Xing, J.L.[Jun-Liang], Zeng, W.J.[Wen-Jun], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data,
ICCV17(2136-2145)
IEEE DOI 1802
image motion analysis, image recognition, recurrent neural nets, 3D skeleton data, LSTM architecture, BibRef

Meng, F., Liu, H., Liang, Y., Tu, J., Liu, M.,
Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition,
IP(28), No. 11, November 2019, pp. 5281-5295.
IEEE DOI 1909
Skeleton, Training, Testing, Deep learning, Transforms, Neural networks, Task analysis, Human action recognition, LSTM BibRef

Tu, J.H.[Juan-Hui], Liu, H.[Hong], Meng, F.Y.[Fan-Yang], Liu, M.Y.[Meng-Yuan], Ding, R.W.[Run-Wei],
Spatial-Temporal Data Augmentation Based on LSTM Autoencoder Network for Skeleton-Based Human Action Recognition,
ICIP18(3478-3482)
IEEE DOI 1809
Skeleton, Training, Data models, Decoding, Neurons, Protocols, 3D Action Recognition, Long Short-Term Memory, Autoencoder BibRef

Ke, Q., Bennamoun, M., Rahmani, H., An, S., Sohel, F.A., Boussaid, F.,
Learning Latent Global Network for Skeleton-Based Action Prediction,
IP(29), No. 1, 2020, pp. 959-970.
IEEE DOI 1910
Skeleton, Videos, Australia, Recurrent neural networks, Lighting, Video sequences, convolutional neural networks BibRef

Cao, C.Q.[Cong-Qi], Lan, C.L.[Cui-Ling], Zhang, Y.F.[Yi-Fan], Zeng, W.J.[Wen-Jun], Lu, H.Q.[Han-Qing], Zhang, Y.N.[Yan-Ning],
Skeleton-Based Action Recognition With Gated Convolutional Neural Networks,
CirSysVideo(29), No. 11, November 2019, pp. 3247-3257.
IEEE DOI 1911
Skeleton, Logic gates, Task analysis, Recurrent neural networks, Matrix converters, convolutional neural networks
See also Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks with Spatiotemporal Transformer Modules. BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Decoupled Spatial-temporal Attention Network for Skeleton-based Action-gesture Recognition,
ACCV20(V:38-53).
Springer DOI 2103
BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], He, X.Y.[Xiang-Yu], Chen, W.H.[Wei-Han], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Skeleton-Based Action Recognition With Shift Graph Convolutional Network,
CVPR20(180-189)
IEEE DOI 2008
Skeleton, Kernel, Convolutional codes, Computational modeling, Adaptation models, Pattern recognition BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks,
IP(29), 2020, pp. 9532-9545.
IEEE DOI 2011
BibRef
Earlier:
Skeleton-Based Action Recognition With Directed Graph Neural Networks,
CVPR19(7904-7913).
IEEE DOI 2002
BibRef
And:
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition,
CVPR19(12018-12027).
IEEE DOI 2002
Adaptation models, Joints, Data models, Bones, Spatiotemporal phenomena, Task analysis, multi-stream network BibRef

Shi, L.[Lei], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Action recognition via pose-based graph convolutional networks with intermediate dense supervision,
PR(121), 2022, pp. 108170.
Elsevier DOI 2109
Action recognition, Skeleton BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], He, X.Y.[Xiang-Yu], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Extremely Lightweight Skeleton-Based Action Recognition with ShiftGCN++,
IP(30), 2021, pp. 7333-7348.
IEEE DOI 2108
Skeleton, Convolutional codes, Image recognition, Computational modeling, Adaptation models, shift network BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], Cao, C.Q.[Cong-Qi], Shi, L.[Lei], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Decoupling GCN with Dropgraph Module for Skeleton-based Action Recognition,
ECCV20(XXIV:536-553).
Springer DOI 2012
BibRef

Zhu, K.J.[Kai-Jun], Wang, R.X.[Ru-Xin], Zhao, Q.S.[Qing-Song], Cheng, J.[Jun], Tao, D.P.[Da-Peng],
A Cuboid CNN Model with an Attention Mechanism for Skeleton-Based Action Recognition,
MultMed(22), No. 11, November 2020, pp. 2977-2989.
IEEE DOI 2010
Feature extraction, Skeleton, Sensors, Spatiotemporal phenomena, Hidden Markov models, Neural networks, feature cuboid BibRef

Liu, J.[Jun], Ding, H.H.[Heng-Hui], Shahroudy, A.[Amir], Duan, L.Y.[Ling-Yu], Jiang, X.D.[Xu-Dong], Wang, G.[Gang], Kot, A.C.[Alex C.],
Feature Boosting Network For 3D Pose Estimation,
PAMI(42), No. 2, February 2020, pp. 494-501.
IEEE DOI 2001
Pose estimation, Boosting, Logic gates, Reliability, context consistency gate BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Xu, D.[Dong], Kot, A.C., Wang, G.[Gang],
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates,
PAMI(40), No. 12, December 2018, pp. 3007-3021.
IEEE DOI 1811
BibRef
Earlier: A1, A2, A3, A5, Only:
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition,
ECCV16(III: 816-833).
Springer DOI 1611
Recurrent neural networks, Spatiotemporal phenomena, Feature extraction, skeleton sequence BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Wang, G.[Gang], Duan, L.Y.[Ling-Yu], Kot, A.C.[Alex C.],
Skeleton-Based Online Action Prediction Using Scale Selection Network,
PAMI(42), No. 6, June 2020, pp. 1453-1467.
IEEE DOI 2005
Skeleton, Task analysis, Videos, Real-time systems, Pattern recognition, skeleton data BibRef

Liu, J.[Jun], Shahroudy, A.[Amir], Wang, G.[Gang], Duan, L.Y.[Ling-Yu], Kot, A.C.[Alex C.],
SSNet: Scale Selection Network for Online 3D Action Prediction,
CVPR18(8349-8358)
IEEE DOI 1812
Convolution, Skeleton, Task analysis, Predictive models, Real-time systems BibRef

Qin, Y.[Yang], Mo, L.F.[Ling-Fei], Li, C.Y.[Chen-Yang], Luo, J.Y.[Jia-Yi],
Skeleton-Based Action Recognition by Part-Aware Graph Convolutional Networks,
VC(36), No. 3, March 2020, pp. 621-631.
WWW Link. 2002
BibRef

Ding, W.W.[Wen-Wen], Li, X.[Xiao], Li, G.[Guang], Wei, Y.S.[Yue-Song],
Global Relational Reasoning with Spatial Temporal Graph Interaction Networks for Skeleton-Based Action Recognition,
SP:IC(83), 2020, pp. 115776.
Elsevier DOI 2003
Deep learning, Graph convolutional network, Convolutional neural networks, Spatio-temporal graph, Message passing BibRef

Ding, W.W.[Wen-Wen], Zhou, G.H.[Guang-Hui], Ding, C.Y.[Chong-Yang], Li, G.[Guang], Liu, K.[Kai],
Graph-based relational reasoning in a latent space for skeleton-based action recognition,
JVCIR(83), 2022, pp. 103410.
Elsevier DOI 2202
Deep learning, Graph neural networks, Graph convolutional network, Message passing, Grassmannian geometry BibRef

Zhu, G.M.[Guang-Ming], Zhang, L.[Liang], Li, H.S.[Hong-Sheng], Shen, P.[Peiyi], Shah, S.A.A.[Syed Afaq Ali], Bennamoun, M.[Mohammed],
Topology-Learnable Graph Convolution for Skeleton-Based Action Recognition,
PRL(135), 2020, pp. 286-292.
Elsevier DOI 2006
Action recognition, Graph convolution, Graph topology, Skeleton BibRef

Wang, N.[Ning], Zhu, G.M.[Guang-Ming], Li, H.S.[Hong-Sheng], Feng, M.T.[Ming-Tao], Zhao, X.[Xia], Ni, L.[Lan], Shen, P.[Peiyi], Mei, L.[Lin], Zhang, L.[Liang],
Exploring Spatio-Temporal Graph Convolution for Video-Based Human-Object Interaction Recognition,
CirSysVideo(33), No. 10, October 2023, pp. 5814-5827.
IEEE DOI Code:
WWW Link. 2310
BibRef

Zhu, G.M.[Guang-Ming], Yang, L.[Lu], Zhang, L.[Liang], Shen, P.[Peiyi], Song, J.[Juan],
Recurrent Graph Convolutional Networks for Skeleton-based Action Recognition,
ICPR21(1352-1359)
IEEE DOI 2105
Deep learning, Network topology, Topology, Pattern recognition BibRef

Avola, D., Cascio, M., Cinque, L., Foresti, G.L., Massaroni, C., RodolÓ, E.,
2-D Skeleton-Based Action Recognition via Two-Branch Stacked LSTM-RNNs,
MultMed(22), No. 10, October 2020, pp. 2481-2496.
IEEE DOI 2009
Skeleton, Feature extraction, Cameras, Recurrent neural networks, long short-term memory (LSTM) BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, J.[Jing], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Action Recognition from Depth Maps Using Deep Convolutional Neural Networks,
HMS(46), No. 4, August 2016, pp. 498-509.
IEEE DOI 1608
data mining BibRef

Miao, S.Y.[Shuang-Yan], Hou, Y.H.[Yong-Hong], Gao, Z.M.[Zhi-Min], Xu, M.L.[Ming-Liang], Li, W.Q.[Wan-Qing],
A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 7, July 2022, pp. 4893-4899.
IEEE DOI 2207
Convolution, Bones, Joints, Convolutional codes, Aggregates, Topology, Training, Graph convolutional network, action recognition, skeleton BibRef

Li, C.K.[Chuan-Kun], Li, S.[Shuai], Gao, Y.B.[Yan-Bo], Guo, L.[Lina], Li, W.Q.[Wan-Qing],
Improved Shift Graph Convolutional Network for Action Recognition With Skeleton,
SPLetters(30), 2023, pp. 438-442.
IEEE DOI 2305
Convolution, Skeleton, Computational complexity, Feature extraction, Convolutional neural networks, Kernel, skeleton BibRef

Li, S.[Shuai], He, X.X.[Xin-Xue], Song, W.F.[Wen-Feng], Hao, A.[Aimin], Qin, H.[Hong],
Graph Diffusion Convolutional Network for Skeleton Based Semantic Recognition of Two-Person Actions,
PAMI(45), No. 7, July 2023, pp. 8477-8493.
IEEE DOI 2306
Feature extraction, Convolution, Semantics, Dynamics, Convolutional neural networks, Bones, two-person interactive actions BibRef

Hou, Y.H.[Yong-Hong], Li, Z.Y.[Zhao-Yang], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks,
CirSysVideo(28), No. 3, March 2018, pp. 807-811.
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, image coding, image colour analysis, image motion analysis, skeleton BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Depth Pooling Based Large-Scale 3-D Action Recognition with Convolutional Neural Networks,
MultMed(20), No. 5, May 2018, pp. 1051-1061.
IEEE DOI 1805
Dynamics, Feature extraction, Gesture recognition, Image recognition, Image segmentation, Motion segmentation, depth BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks,
SPLetters(24), No. 5, May 2017, pp. 624-628.
IEEE DOI 1704
image colour analysis BibRef

Li, C.K.[Chuan-Kun], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing],
Multiview-Based 3-D Action Recognition Using Deep Networks,
HMS(49), No. 1, February 2019, pp. 95-104.
IEEE DOI 1901
Skeleton, Trajectory, Feature extraction, Recurrent neural networks, Image color analysis, Encoding, three dimensional (3-D) BibRef

Wang, P.C.[Pi-Chao], Li, W.Q.[Wan-Qing], Gao, Z.M.[Zhi-Min], Zhang, Y.Y.[Yu-Yao], Tang, C.[Chang], Ogunbona, P.O.[Philip O.],
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks,
CVPR17(416-425)
IEEE DOI 1711
Cameras, Feature extraction, Kernel, Optical imaging, Transforms, Videos BibRef

Zhang, J.[Jing], Li, W.Q.[Wan-Qing], Wang, P.C.[Pi-Chao], Ogunbona, P.[Philip], Liu, S.[Song], Tang, C.[Chang],
A Large Scale RGB-D Dataset for Action Recognition,
UHA3DS16(101-114).
Springer DOI 1806
BibRef

Zhang, H.Y.[Hao-Yuan], Hou, Y.H.[Yong-Hong], Wang, P.C.[Pi-Chao], Guo, Z.H.[Zi-Hui], Li, W.Q.[Wan-Qing],
SAR-NAS: Skeleton-based action recognition via neural architecture searching,
JVCIR(73), 2020, pp. 102942.
Elsevier DOI 2012
Neural architecture search, Action recognition, Skeleton
See also Large-Scale Continuous Gesture Recognition Using Convolutional Neural Networks. BibRef

Huang, H.[Hong'en], Su, H.[Hang], Chang, Z.G.[Zhi-Gang], Yu, M.Y.[Ming-Yang], Gao, J.L.[Jia-Lin], Li, X.Z.[Xin-Zhe], Zheng, S.B.[Shi-Bao],
Convolutional neural network with adaptive inferential framework for skeleton-based action recognition,
JVCIR(73), 2020, pp. 102925.
Elsevier DOI 2012
Skeleton-based action recognition, Pseudo image, Adaptive inferential framework, Different prior information BibRef

Gao, J.L.[Jia-Lin], He, T.[Tong], Zhou, X.[Xi], Ge, S.M.[Shi-Ming],
Skeleton-Based Action Recognition With Focusing-Diffusion Graph Convolutional Networks,
SPLetters(28), 2021, pp. 2058-2062.
IEEE DOI 2111
Focusing, Convolution, Skeleton, Transformers, Hidden Markov models, Context modeling, Aggregates, Focusing and diffusion, action recognition BibRef

Liu, K., Gao, L., Khan, N.M., Qi, L., Guan, L.,
A Multi-Stream Graph Convolutional Networks-Hidden Conditional Random Field Model for Skeleton-Based Action Recognition,
MultMed(23), 2021, pp. 64-76.
IEEE DOI 2012
Feature extraction, Convolution, Adaptation models, Neural networks, Bones, Message passing, GCN, CRF, skeleton, action recognition BibRef

Peng, W.[Wei], Hong, X.P.[Xiao-Peng], Zhao, G.Y.[Guo-Ying],
Tripool: Graph triplet pooling for 3D skeleton-based action recognition,
PR(115), 2021, pp. 107921.
Elsevier DOI 2104
3D skeletal action recognition, ST-GCN, Graph pooling, Graph topology analysis BibRef

Peng, W.[Wei], Shi, J.G.[Jin-Gang], Zhao, G.Y.[Guo-Ying],
Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition,
SPLetters(28), 2021, pp. 244-248.
IEEE DOI 2102
Deconvolution, Convolution, Kernel, Skeleton, Task analysis, Covariance matrices, Correlation, Graph neural network, over-smoothing BibRef

Hao, X.K.[Xiao-Ke], Li, J.[Jie], Guo, Y.C.[Ying-Chun], Jiang, T.[Tao], Yu, M.[Ming],
Hypergraph Neural Network for Skeleton-Based Action Recognition,
IP(30), 2021, pp. 2263-2275.
IEEE DOI 2102
convolutional neural nets, feature extraction, Fourier analysis, graph theory, image fusion, geometric relations BibRef

Sun, N.[Ning], Leng, L.[Ling], Liu, J.X.[Ji-Xin], Han, G.[Guang],
Multi-stream slowFast graph convolutional networks for skeleton-based action recognition,
IVC(109), 2021, pp. 104141.
Elsevier DOI 2105
Action recognition, Graph convolutional network, Human skeleton, SlowFast network, Attention BibRef

Plizzari, C.[Chiara], Cannici, M.[Marco], Matteucci, M.[Matteo],
Skeleton-based action recognition via spatial and temporal transformer networks,
CVIU(208-209), 2021, pp. 103219.
Elsevier DOI 2106
BibRef
Earlier:
Spatial Temporal Transformer Network for Skeleton-based Action Recognition,
FBE20(694-701).
Springer DOI 2103
Representation learning, Graph CNN, Self-attention, 3D skeleton, Action recognition BibRef

Banerjee, A.[Avinandan], Singh, P.K.[Pawan Kumar], Sarkar, R.[Ram],
Fuzzy Integral-Based CNN Classifier Fusion for 3D Skeleton Action Recognition,
CirSysVideo(31), No. 6, June 2021, pp. 2206-2216.
IEEE DOI 2106
Skeleton, Feature extraction, Kinematics, Data mining, Image coding, convolutional neural network BibRef

Li, X.M.[Xing-Ming], Zhai, W.[Wei], Cao, Y.[Yang],
A tri-attention enhanced graph convolutional network for skeleton-based action recognition,
IET-CV(15), No. 2, 2021, pp. 110-121.
DOI Link 2106
BibRef

Yu, B.X.B.[Bruce X.B.], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.], Yang, Q.[Qintai], Wang, X.Y.[Xiao-Ying],
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer's progression,
PR(119), 2021, pp. 108095.
Elsevier DOI 2108
Human action evaluation, Alzheimer's disease, Graph neural network, Abnormality detection BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Zhao, Y.H.[Yang-Heng], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction,
IP(30), 2021, pp. 7760-7775.
IEEE DOI 2109
BibRef
Earlier:
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction,
CVPR20(211-220)
IEEE DOI 2008
Feature extraction, Decoding, Predictive models, Convolution, Dynamics, Computational modeling, graph convolution. Dynamics, Convolution, Neural networks, Adaptation models BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Zhang, Z.J.[Zi-Jing], Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Zhang, Y.[Ya],
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction,
ECCV22(VI:18-36).
Springer DOI 2211
BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Chen, X.[Xu], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction,
PAMI(44), No. 6, June 2022, pp. 3316-3333.
IEEE DOI 2205
Feature extraction, Magnetic heads, Joints, Convolution, Task analysis, Symbiosis, graph inference BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Liu, Z.H.[Zi-Hui], Zhang, Z.J.[Zi-Jing], Xie, L.X.[Ling-Xi], Tian, Q.[Qi], Zhang, Y.[Ya],
Skeleton Graph Scattering Networks for 3D Skeleton-based Human Motion Prediction,
GSP-CV21(854-864)
IEEE DOI 2112
Convolution, Aggregates, Scattering, Feature extraction BibRef

Feng, H.[Hui], Wang, S.S.[Shan-Shan], Xu, H.X.[Hai-Xiang], Ge, S.S.[Shuzhi Sam],
Object Activity Scene Description, Construction, and Recognition,
Cyber(51), No. 10, October 2021, pp. 5082-5092.
IEEE DOI 2110
Skeleton, Feature extraction, Hip, Cybernetics, Trajectory, Data mining, Histograms, Convolutional neural network (CNN), scene recognition BibRef

Yang, H.[Hao], Yan, D.[Dan], Zhang, L.[Li], Sun, Y.[Yunda], Li, D.[Dong], Maybank, S.J.[Stephen J.],
Feedback Graph Convolutional Network for Skeleton-Based Action Recognition,
IP(31), 2022, pp. 164-175.
IEEE DOI 2112
Skeleton, Feature extraction, Joints, Semantics, Predictive models, Data models, Convolution, Feedback mechanism, action recognition BibRef

Naveenkumar, M., Domnic, S.,
Spatio Temporal Joint Distance Maps for Skeleton-Based Action Recognition Using Convolutional Neural Networks,
IJIG(21), No. 5 2021, pp. 2140001.
DOI Link 2201
BibRef

Koniusz, P.[Piotr], Wang, L.[Lei], Cherian, A.[Anoop],
Tensor Representations for Action Recognition,
PAMI(44), No. 2, February 2022, pp. 648-665.
IEEE DOI 2201
Tensors, Kernel, Skeleton, Correlation, Optical imaging, Higher order statistics, CNN, 3D skeletons, power normalization BibRef

Koniusz, P.[Piotr], Cherian, A.[Anoop], Porikli, F.M.[Fatih M.],
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons,
ECCV16(IV: 37-53).
Springer DOI 1611
BibRef
Earlier: A1, A2, Only:
Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors with Application to Texture Recognition,
CVPR16(5395-5403)
IEEE DOI 1612
BibRef

Alsarhan, T.[Tamam], Ali, U.[Usman], Lu, H.T.[Hong-Tao],
Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition,
CVIU(216), 2022, pp. 103348.
Elsevier DOI 2202
Skeleton-based action recognition, Graph convolutional network, Squeeze and excitation, Adaptive temporal modelling BibRef

Yu, L.[Lubin], Tian, L.F.[Lian-Fang], Du, Q.L.[Qi-Liang], Bhutto, J.A.[Jameel Ahmed],
Multi-stream adaptive spatial-temporal attention graph convolutional network for skeleton-based action recognition,
IET-CV(16), No. 2, 2022, pp. 143-158.
DOI Link 2202
computer graphics, convolutional neural nets, graphics processing units, space-time adaptive processing BibRef

Tang, J.[Jun], Wang, Y.J.[Yan-Jiang], Fu, S.C.[Si-Chao], Liu, B.D.[Bao-Di], Liu, W.F.[Wei-Feng],
A graph convolutional neural network model with Fisher vector encoding and channel-wise spatial-temporal aggregation for skeleton-based action recognition,
IET-IPR(16), No. 5, 2022, pp. 1433-1443.
DOI Link 2203
BibRef

Xie, Y.L.[Yu-Lai], Zhang, Y.[Yang], Ren, F.[Fang],
Temporal-Enhanced Graph Convolution Network for Skeleton-Based Action Recognition,
IET-CV(16), No. 3, 2022, pp. 266-279.
DOI Link 2204
causal convolution, graph convolution network, long-range temporal correlation, temporal sequence modelling BibRef

Wu, C.[Cong], Wu, X.J.[Xiao-Jun], Kittler, J.V.[Josef V.],
Graph2Net: Perceptually-Enriched Graph Learning for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 4, April 2022, pp. 2120-2132.
IEEE DOI 2204
BibRef
Earlier:
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition,
SGRL19(1740-1748)
IEEE DOI 2004
Skeleton, Hidden Markov models, Feature extraction, Spatiotemporal phenomena, Convolution, Technological innovation, graph learning. convolutional neural nets, feature extraction, graph theory, image fusion, image representation, spatiotemporal phenomena, Skeleton Based Action Recognition BibRef

Shu, X.B.[Xiang-Bo], Zhang, L.Y.[Li-Yan], Qi, G.J.[Guo-Jun], Liu, W.[Wei], Tang, J.H.[Jin-Hui],
Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction,
PAMI(44), No. 6, June 2022, pp. 3300-3315.
IEEE DOI 2205
Skeleton, Predictive models, Spatiotemporal phenomena, Solid modeling, Recurrent neural networks, Spatial coherence, recurrent neural network BibRef

Zheng, H.[Hui], Zhang, X.M.[Xin-Ming],
A Cross View Learning Approach for Skeleton-Based Action Recognition,
CirSysVideo(32), No. 5, May 2022, pp. 3061-3072.
IEEE DOI 2205
Convolution, Joints, Task analysis, Feature extraction, Data models, Bones, Recurrent neural networks, HAR, fusion, inter-view, multi-scale, skeleton BibRef

Qin, X.F.[Xiao-Fei], Li, H.[Hao], Liu, Y.[Yuru], Yu, J.B.[Jia-Bin], He, C.X.[Chang-Xiang], Zhang, X.[Xuedian],
Multi-stage part-aware graph convolutional network for skeleton-based action recognition,
IET-IPR(16), No. 8, 2022, pp. 2063-2074.
DOI Link 2205
BibRef

Xu, B.Q.[Bin-Qian], Shu, X.B.[Xiang-Bo], Song, Y.[Yan],
X-Invariant Contrastive Augmentation and Representation Learning for Semi-Supervised Skeleton-Based Action Recognition,
IP(31), 2022, pp. 3852-3867.
IEEE DOI 2206
Skeleton, Representation learning, Joints, Bones, Semisupervised learning, Recurrent neural networks, contrastive learning BibRef

Shu, X.B.[Xiang-Bo], Xu, B.Q.[Bin-Qian], Zhang, L.Y.[Li-Yan], Tang, J.H.[Jin-Hui],
Multi-Granularity Anchor-Contrastive Representation Learning for Semi-Supervised Skeleton-Based Action Recognition,
PAMI(45), No. 6, June 2023, pp. 7559-7576.
IEEE DOI 2305
Skeleton, Task analysis, Loss measurement, Joints, Semantics, Data models, Pattern recognition, Action recognition, skeleton, anchor graph BibRef

Wu, K.L.[Kun-Lun], Gong, X.[Xun],
Dynamic Channel-Aware Subgraph Interactive Networks for Skeleton-Based Action Recognition,
SPLetters(29), 2022, pp. 2592-2596.
IEEE DOI 2301
Skeleton, Topology, Solid modeling, Adaptation models, Convolution, Computational modeling, Collaboration, Graph neural network, skeleton-based action recognition BibRef

Xiong, X.[Xin], Min, W.D.[Wei-Dong], Wang, Q.[Qi], Zha, C.[Cheng],
Human Skeleton Feature Optimizer and Adaptive Structure Enhancement Graph Convolution Network for Action Recognition,
CirSysVideo(33), No. 1, January 2023, pp. 342-353.
IEEE DOI 2301
Feature extraction, Skeleton, Convolution, Data mining, Directed graphs, Smart cities, Kernel, Action recognition, adaptive pooling operation BibRef

Zhu, Y.S.[Yi-Sheng], Shuai, H.[Hui], Liu, G.C.[Guang-Can], Liu, Q.S.[Qing-Shan],
Multilevel Spatial-Temporal Excited Graph Network for Skeleton-Based Action Recognition,
IP(32), 2023, pp. 496-508.
IEEE DOI 2301
Skeleton, Convolution, Topology, Head, Feature extraction, Biological system modeling, Transformers, multilevel spatial-temporal modeling BibRef

Wu, L.[Liyu], Zhang, C.[Can], Zou, Y.X.[Yue-Xian],
SpatioTemporal focus for skeleton-based action recognition,
PR(136), 2023, pp. 109231.
Elsevier DOI 2301
Action recognition, Skeleton topology, Graph convolutional network BibRef

Weng, L.[Libo], Lou, W.D.[Wei-Dong], Shen, X.[Xin], Gao, F.[Fei],
A 3D Graph Convolutional Networks Model for 2D Skeleton-Based Human Action Recognition,
IET-IPR(17), No. 3, 2023, pp. 773-783.
DOI Link 2303
2D human action recognition, 3D convolutional neural networks, attention mechanism, graph convolutional neural networks, skeleton sequences BibRef

Zhang, S.B.[Shao-Bo], Liu, S.[Sheng], Gao, F.[Fei],
3D Human Motion Prediction via Activity-Driven Attention-MLP Association,
ICIP23(960-964)
IEEE DOI Code:
WWW Link. 2312
BibRef

Bian, C.L.[Cun-Ling], Feng, W.[Wei], Meng, F.[Fanbo], Wang, S.[Song],
Global-local contrastive multiview representation learning for skeleton-based action recognition,
CVIU(229), 2023, pp. 103655.
Elsevier DOI 2303
Skeleton-based action recognition, Contrastive representation learning, Multiview, Graph convolutional network BibRef

Huang, Z.X.[Zeng-Xi], Qin, Y.S.[Yu-Song], Lin, X.B.[Xia-Bing], Liu, T.L.[Tian-Lin], Feng, Z.H.[Zhen-Hua], Liu, Y.G.[Yi-Guang],
Motion-Driven Spatial and Temporal Adaptive High-Resolution Graph Convolutional Networks for Skeleton-Based Action Recognition,
CirSysVideo(33), No. 4, April 2023, pp. 1868-1883.
IEEE DOI 2304
Skeleton, Feature extraction, Convolution, Adaptation models, Joints, Data mining, Correlation, Graph convolutional networks, high-resolution graph BibRef

Hedegaard, L.[Lukas], Heidari, N.[Negar], Iosifidis, A.[Alexandros],
Continual spatio-temporal graph convolutional networks,
PR(140), 2023, pp. 109528.
Elsevier DOI 2305
Graph convolutional networks, Continual inference, Efficient deep learning, Skeleton-based action recognition BibRef

Wang, M.[Minsi], Ni, B.B.[Bing-Bing], Yang, X.K.[Xiao-Kang],
Learning Multi-View Interactional Skeleton Graph for Action Recognition,
PAMI(45), No. 6, June 2023, pp. 6940-6954.
IEEE DOI 2305
Skeleton, Topology, Feature extraction, Convolution, Network topology, Recurrent neural networks, Action recognition, hierarchical method BibRef

Tu, Z.G.[Zhi-Gang], Zhang, J.X.[Jia-Xu], Li, H.Y.[Hong-Yan], Chen, Y.J.[Yu-Jin], Yuan, J.S.[Jun-Song],
Joint-Bone Fusion Graph Convolutional Network for Semi-Supervised Skeleton Action Recognition,
MultMed(25), 2023, pp. 1819-1831.
IEEE DOI 2306
Joints, Bones, Semisupervised learning, Feature extraction, Correlation, Convolution, Training data, Action recognition, skeleton action BibRef

Hadikhani, P.[Parham], Lai, D.T.C.[Daphne Teck Ching], Ong, W.H.[Wee-Hong],
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization With Gaussian Mutation,
HMS(53), No. 3, June 2023, pp. 538-548.
IEEE DOI 2306
Feature extraction, Data mining, Principal component analysis, Clustering algorithms, Training, Convolutional neural networks, unsupervised learning BibRef

Hadikhani, P.[Parham], Lai, D.T.C.[Daphne Teck Ching], Ong, W.H.[Wee-Hong],
Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory,
MultMed(26), 2024, pp. 420-435.
IEEE DOI 2402
Optimization, Clustering algorithms, Game theory, Videos, Linear programming, Feature extraction, Human activity discovery, skeleton sequence BibRef

Hadikhani, P.[Parham], Lai, D.T.C.[Daphne Teck Ching], Ong, W.H.[Wee-Hong],
Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised,
IVC(145), 2024, pp. 104985.
Elsevier DOI 2405
Human activity discovery, Unsupervised learning, Clustering, Feature extraction, Incremental manner, Skeleton sequence BibRef

Bavil, A.F.[Ali Farajzadeh], Damirchi, H.[Hamed], Taghirad, H.D.[Hamid D.],
Action Capsules: Human skeleton action recognition,
CVIU(233), 2023, pp. 103722.
Elsevier DOI 2307
Skeleton-based human action recognition, Capsule neural network, Action capsules, Global action capsules BibRef

Ahmad, T.[Tasweer], Rizvi, S.T.H.[Syed Tahir Hussain], Kanwal, N.[Neel],
Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition,
JVCIR(95), 2023, pp. 103892.
Elsevier DOI 2309
Human action recognition, Graph convolutional network, Link prediction, Self-attention, Computer vision BibRef

Friji, R.[Rasha], Chaieb, F.[Faten], Drira, H.[Hassen], Kurtek, S.[Sebastian],
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Landmark-Based Human Behavior Analysis,
PAMI(45), No. 11, November 2023, pp. 13314-13327.
IEEE DOI 2310
BibRef

Friji, R.[Rasha], Drira, H.[Hassen], Chaieb, F.[Faten], Kchok, H.[Hamza], Kurtek, S.[Sebastian],
Geometric Deep Neural Network using Rigid and Non-Rigid Transformations for Human Action Recognition,
ICCV21(12591-12600)
IEEE DOI 2203
Deep learning, Shape, Neural networks, Skeleton, Action and behavior recognition, Motion and tracking, Representation learning BibRef

Yang, P.[Ping], Wang, Q.[Qin], Chen, H.[Hao], Wu, Z.Z.[Zi-Zhao],
Position-aware spatio-temporal graph convolutional networks for skeleton-based action recognition,
IET-CV(17), No. 7, 2023, pp. 844-854.
DOI Link 2310
computer vision, convolutional neural nets, graph theory BibRef

Geng, P.[Pei], Lu, X.Q.[Xue-Quan], Hu, C.Y.[Chun-Yu], Liu, H.[Hong], Lyu, L.[Lei],
Focusing Fine-Grained Action by Self-Attention-Enhanced Graph Neural Networks With Contrastive Learning,
CirSysVideo(33), No. 9, September 2023, pp. 4754-4768.
IEEE DOI 2310
BibRef

Zhao, Y.[Yinan], Gao, Q.[Qing], Ju, Z.J.[Zhao-Jie], Zhou, J.[Jian], Guo, Y.L.[Yu-Lan],
Sharing-Net: Lightweight feedforward network for skeleton-based action recognition based on information sharing mechanism,
PR(146), 2024, pp. 110050.
Elsevier DOI 2311
Skeleton-based action recognition, Lightweight structure, Multi-feature input, Information sharing mechanism BibRef

Tian, H.Y.[Hao-Yu], Ma, X.[Xin], Li, X.[Xiang], Li, Y.B.[Yi-Bin],
Skeleton-Based Action Recognition with Select-Assemble-Normalize Graph Convolutional Networks,
MultMed(25), 2023, pp. 8527-8538.
IEEE DOI 2312
BibRef

Wang, X.S.[Xin-Shun], Zhang, W.Y.[Wan-Ying], Wang, C.[Can], Gao, Y.[Yuan], Liu, M.Y.[Meng-Yuan],
Dynamic Dense Graph Convolutional Network for Skeleton-Based Human Motion Prediction,
IP(33), 2024, pp. 1-15.
IEEE DOI 2312
BibRef

Liu, J.F.[Jin-Fu], Wang, X.S.[Xin-Shun], Wang, C.[Can], Gao, Y.[Yuan], Liu, M.Y.[Meng-Yuan],
Temporal Decoupling Graph Convolutional Network for Skeleton-Based Gesture Recognition,
MultMed(26), 2024, pp. 811-823.
IEEE DOI 2402
Skeleton, Gesture recognition, Topology, Feature extraction, Convolutional neural networks, Network topology, Convolution, skeleton sequence BibRef

Guo, J.J.[Jia-Jun], Ji, Q.G.[Qing-Ge], Shan, G.W.[Guang-Wei],
Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition,
IET-IPR(18), No. 1, 2024, pp. 233-246.
DOI Link 2401
computer vision, convolutional neural nets, graph theory, signal denoising BibRef

Lin, G.C.[Guo-Cheng], Sun, Y.[Yue],
Intra-Inter Region Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition,
JVCIR(98), 2024, pp. 104020.
Elsevier DOI 2402
Skeleton-based action recognition, Self-attention, Graph convolution BibRef

Wang, M.D.[Ming-Dao], Li, X.M.[Xue-Ming], Chen, S.Q.[Si-Qi], Zhang, X.L.[Xian-Lin], Ma, L.[Lei], Zhang, Y.[Yue],
Learning Representations by Contrastive Spatio-Temporal Clustering for Skeleton-Based Action Recognition,
MultMed(26), 2024, pp. 3207-3220.
IEEE DOI 2402
Semantics, Training, Data augmentation, Task analysis, Representation learning, Bones, Indexes, Contrastive learning, spatio-temporal clustering BibRef

Li, X.F.[Xuan-Feng], Lu, J.[Jian], Chen, X.G.[Xiao-Gai], Zhang, X.D.[Xiao-Dan],
Spatial-Temporal Adaptive Metric Learning Network for One-Shot Skeleton-Based Action Recognition,
SPLetters(31), 2024, pp. 321-325.
IEEE DOI 2402
Measurement, Encoding, Training, Feature extraction, Protocols, Computational modeling, Adaptive systems, Skeleton data, metric learning BibRef

Huang, Q.[Qian], Liu, W.T.[Wen-Ting], Shang, M.Z.[Ming-Zhou], Wang, Y.M.[Yi-Ming],
Fusing angular features for skeleton-based action recognition using multi-stream graph convolution network,
IET-IPR(18), No. 7, 2024, pp. 1694-1709.
DOI Link 2405
computer vision, video signal processing BibRef

Huang, Q.[Qian], Xie, M.T.[Meng-Ting], Li, X.[Xing], Wang, S.C.[Shuai-Chen],
Skeleton Action Recognition Based on Spatio-Temporal Features,
ICIP23(3284-3288)
IEEE DOI 2312
BibRef

Li, C.[Chang], Mao, Y.C.[Ying-Chi], Huang, Q.[Qian], Zhu, X.W.[Xiao-Wei], Wu, J.[Jie],
Scale-Aware Graph Convolutional Network with Part-Level Refinement for Skeleton-Based Human Action Recognition,
CirSysVideo(34), No. 6, June 2024, pp. 4311-4324.
IEEE DOI 2406
Feature extraction, Convolution, Topology, Joints, Semantics, Bones, Writing, Action recognition, graph convolutional networks, multi-scale analysis BibRef

Huang, Q.[Qian], Nie, Y.Q.[Yun-Qing], Li, X.[Xing], Yang, T.J.[Tian-Jin],
Part Aware Graph Convolution Network with Temporal Enhancement for Skeleton-Based Action Recognition,
ICIP23(3255-3259)
IEEE DOI 2312
BibRef

Shang, M.Z.[Ming-Zhou], Huang, Q.[Qian], Wang, Y.M.[Yi-Ming], Bian, X.[Xiang], Jiang, C.X.[Chuan-Xu], Liu, J.W.[Ji-Wen],
Skeleton-Based Dumbbell Fitness Action Recognition Using Two-Stream LSTM Network,
ICIVC22(31-36)
IEEE DOI 2301
Technological innovation, Image recognition, Clustering algorithms, Feature extraction, Skeleton, LSTM BibRef

Moutik, O.[Oumaima], Sekkat, H.[Hiba], Tchakoucht, T.A.[Taha Ait], El Kari, B.[Badr], Alaoui, A.E.[Ahmed El_Hilali],
A puzzle questions form training for self-supervised skeleton-based action recognition,
IVC(148), 2024, pp. 105137.
Elsevier DOI 2407
Skeleton-based action recognition, Self-supervised learning, Solving pretext task, Self-supervised skeleton-based action recognition BibRef

Zhao, Z.[Zhifu], Chen, Z.W.[Zi-Wei], Li, J.A.[Jian-An], Wang, X.T.[Xiao-Tian], Xie, X.M.[Xue-Mei], Huang, L.[Lei], Zhang, W.X.[Wan-Xin], Shi, G.M.[Guang-Ming],
Glimpse and Zoom: Spatio-Temporal Focused Dynamic Network for Skeleton-Based Action Recognition,
CirSysVideo(34), No. 7, July 2024, pp. 5616-5629.
IEEE DOI 2407
Skeleton, Feature extraction, Data mining, Dynamics, Computer architecture, Proposals, Computational efficiency, reinforcement learning BibRef


Xiang, W.M.[Wang-Meng], Li, C.[Chao], Zhou, Y.X.[Yu-Xuan], Wang, B.[Biao], Zhang, L.[Lei],
Generative Action Description Prompts for Skeleton-based Action Recognition,
ICCV23(10242-10251)
IEEE DOI Code:
WWW Link. 2401
BibRef

Liu, X.Y.[Xing-Yu], Zhou, S.P.[San-Ping], Wang, L.[Le], Hua, G.[Gang],
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition,
ICCV23(1379-1388)
IEEE DOI 2401
BibRef

Kang, M.S.[Min-Seok], Kang, D.[Dongoh], Kim, H.S.[Han-Saem],
Efficient Skeleton-Based Action Recognition via Joint-Mapping strategies,
WACV23(3392-3401)
IEEE DOI 2302
Visualization, Embedded systems, Computational modeling, Surveillance, Pose estimation, Real-time systems, Robotics BibRef

Cho, S.[Sangwoo], Maqbool, M.H.[Muhammad Hasan], Liu, F.[Fei], Foroosh, H.[Hassan],
Self-Attention Network for Skeleton-based Human Action Recognition,
WACV20(624-633)
IEEE DOI 2006
Skeleton, Semantics, Data mining, Recurrent neural networks, Computational modeling, Encoding BibRef

Li, S., Jiang, T., Huang, T., Tian, Y.,
Global Co-occurrence Feature Learning and Active Coordinate System Conversion for Skeleton-based Action Recognition,
WACV20(575-583)
IEEE DOI 2006
Skeleton, Feature extraction, Convolution, Solid modeling, Recurrent neural networks, Head BibRef

Hang, R.[Rui], Li, M.X.[Min-Xian],
Spatial-temporal Adaptive Graph Convolutional Network for Skeleton-based Action Recognition,
ACCV22(IV:172-188).
Springer DOI 2307
BibRef

Shen, J.X.[Jun-Xiao], Dudley, J.[John], Kristensson, P.O.[Per Ola],
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition,
FG21(1-8)
IEEE DOI 2303
Deep learning, Training data, Stochastic processes, Inspection, Generative adversarial networks, Data models, Spatiotemporal phenomena BibRef

Bandi, C.[Chaitanya], Thomas, U.[Ulrike],
Skeleton-based Action Recognition for Human-Robot Interaction using Self-Attention Mechanism,
FG21(1-8)
IEEE DOI 2303
Recurrent neural networks, Pipelines, Human-robot interaction, Predictive models, Encoding, Skeleton, Real-time systems BibRef

Chen, T.[Tailin], Zhou, D.[Desen], Wang, J.[Jian], Wang, S.D.[Shi-Dong], He, Q.[Qian], Hu, C.Y.[Chuan-Yang], Ding, E.[Errui], Guan, Y.[Yu], He, X.M.[Xu-Ming],
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition,
FG23(1-8)
IEEE DOI 2303
Visualization, Fuses, Face recognition, Prototypes, Gesture recognition, Benchmark testing, Skeleton BibRef

Zhu, A.[Anqi], Ke, Q.H.[Qiu-Hong], Gong, M.M.[Ming-Ming], Bailey, J.[James],
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition,
WACV23(6027-6036)
IEEE DOI 2302
Training, Adaptation models, Visualization, Adaptive systems, Measurement units, Face recognition BibRef

Liu, C.[Cuiwei], Zhao, X.X.[Xiao-Xue], Yan, Z.[Zhuo], Jiang, Y.Z.[You-Zhi], Shi, X.B.[Xiang-Bin],
A Graph Convolutional Network with Early Attention Module for Skeleton-based Action Prediction,
ICPR22(1266-1272)
IEEE DOI 2212
Convolution, Feature extraction, Skeleton, Character recognition, Task analysis BibRef

Xing, H.[Hao], Burschka, D.[Darius],
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional Network,
ICPR22(3333-3340)
IEEE DOI 2212
Adaptation models, Head, Skeleton, Natural language processing, Graph neural networks, Planning, Kinetic theory BibRef

Tang, Z.H.[Zhi-Hao], Xia, H.L.[Hai-Lun], Gao, X.K.[Xin-Kai], Gao, F.[Feng], Feng, C.Y.[Chun-Yan],
Skeleton-Based Action Recognition with Graph Involution Network,
ICPR22(3348-3354)
IEEE DOI 2212
Spirals, Convolution, Network topology, Benchmark testing, Logic gates, Skeleton, Topology BibRef

Hao, Y.L.[Yan-Ling], Shi, Z.Y.[Zhi-Yuan], Liu, Y.[Yuanwei],
WiFi-Based Spatiotemporal Human Action Perception,
ICIP22(3581-3585)
IEEE DOI 2211
Support vector machines, Visualization, Neural networks, Line-of-sight propagation, Benchmark testing, Skeleton, wireless-vision BibRef

Kilis, N.[Nikolaos], Papaioannidis, C.[Christos], Mademlis, I.[Ioannis], Pitas, I.[Ioannis],
An Efficient Framework for Human Action Recognition Based on Graph Convolutional Networks,
ICIP22(1441-1445)
IEEE DOI 2211
Image recognition, Convolution, Architecture, Pipelines, Skeleton, Skeleton-based human action recognition, feature imputation BibRef

Liu, Y.[Yan], Deng, Y.L.[Yue-Lin], Su, J.P.[Jin-Ping], Wang, R.N.[Ruo-Nan], Li, C.[Chi],
Multiple Input Branches Shift Graph Convolutional Network with DropEdge for Skeleton-Based Action Recognition,
CIAP22(I:584-596).
Springer DOI 2205
BibRef

Villegas, R.[Ruben], Ceylan, D.[Duygu], Hertzmann, A.[Aaron], Yang, J.[Jimei], Saito, J.[Jun],
Contact-Aware Retargeting of Skinned Motion,
ICCV21(9700-9709)
IEEE DOI 2203
Torso, Geometry, Recurrent neural networks, Shape, Motion estimation, Skeleton, Encoding, Motion and tracking, Gestures and body pose BibRef

Su, Y.K.[Yu-Kun], Lin, G.S.[Guo-Sheng], Wu, Q.Y.[Qing-Yao],
Self-supervised 3D Skeleton Action Representation Learning with Motion Consistency and Continuity,
ICCV21(13308-13318)
IEEE DOI 2203
Representation learning, Interpolation, Dynamics, Transfer learning, Force, Network architecture, BibRef

Chen, Y.X.[Yu-Xin], Zhang, Z.Q.[Zi-Qi], Yuan, C.F.[Chun-Feng], Li, B.[Bing], Deng, Y.[Ying], Hu, W.M.[Wei-Ming],
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition,
ICCV21(13339-13348)
IEEE DOI 2203
Correlation, Network topology, Convolution, Computational modeling, Aggregates, Refining, Action and behavior recognition, BibRef

Nguyen, X.S.[Xuan Son],
GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition,
ICCV21(13359-13369)
IEEE DOI 2203
Geometry, Manifolds, Symmetric matrices, Neural networks, Gaussian distribution, Action and behavior recognition, Video analysis and understanding BibRef

Huynh-The, T.[Thien], Hua, C.H.[Cam-Hao], Tu, N.A.[Nguyen Anh], Kim, D.S.[Dong-Seong],
Space-Time Skeletal Analysis with Jointly Dual-Stream ConvNet for Action Recognition,
DICTA20(1-7)
IEEE DOI 2201
Training, Image recognition, Dynamics, Skeleton, Kernel, Action recognition, convolutional network, 3D skeleton data BibRef

Lie, W.N.[Wen-Nung], Huang, Y.J.[Yong-Jhu], Chiang, J.C.[Jui-Chiu], Fang, Z.Y.[Zhen-Yu],
High-Order Joint Information Input for Graph Convolutional Network Based Action Recognition,
ICIP21(1064-1068)
IEEE DOI 2201
Couplings, Protocols, Fuses, Convolution, Image edge detection, Deep learning, action recognition, graph convolutional network, 3D human skeleton BibRef

Ban Teng, M.L.[Michael Lao], Wu, Z.Y.[Zhi-Yong],
Channel-Wise Dense Connection Graph Convolutional Network for Skeleton-Based Action Recognition,
ICPR21(3799-3806)
IEEE DOI 2105
Legged locomotion, Adaptation models, Time series analysis, Feature extraction, Data models, Robustness, Kinetic theory BibRef

Nam, S.[Suekyeong], Lee, S.K.[Seung-Kyu],
JT-MGCN: Joint-temporal Motion Graph Convolutional Network for Skeleton-Based Action Recognition,
ICPR21(6383-6390)
IEEE DOI 2105
Correlation, Skeleton, Pattern recognition BibRef

Heidari, N.[Negar], Iosifidis, A.[Alexandros],
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition,
ICPR21(7907-7914)
IEEE DOI 2105
Network topology, Computational modeling, Benchmark testing, Skeleton, Data models, Distance measurement, Pattern recognition BibRef

Shiraki, K.[Katsutoshi], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Fujiyoshi, H.[Hironobu],
Spatial Temporal Attention Graph Convolutional Networks with Mechanics-stream for Skeleton-based Action Recognition,
ACCV20(V:341-357).
Springer DOI 2103
BibRef

Corona, E., Pumarola, A., AlenyÓ, G., Moreno-Noguer, F.,
Context-Aware Human Motion Prediction,
CVPR20(6990-6999)
IEEE DOI 2008
Predictive models, Task analysis, Skeleton, Recurrent neural networks, Semantics, Context modeling BibRef

Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.,
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition,
CVPR20(140-149)
IEEE DOI 2008
Feature extraction, Joints, Robustness, Bones, Pattern recognition, Correlation BibRef

Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., Zheng, N.,
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition,
CVPR20(1109-1118)
IEEE DOI 2008
Skeleton, Semantics, Indexes, Neural networks, Computational modeling, Correlation BibRef

Huang, J.Q.[Jun-Qin], Huang, Z.H.[Zhen-Huan], Xiang, X.[Xiang], Gong, X.[Xuan], Zhang, B.C.[Bao-Chang],
Long-Short Graph Memory Network for Skeleton-Based Action Recognition,
WACV20(634-641)
IEEE DOI 2006
Feature extraction, Convolution, Skeleton, Calibration, Data models, Data mining, Neural networks BibRef

Yan, S., Li, Z., Xiong, Y., Yan, H., Lin, D.,
Convolutional Sequence Generation for Skeleton-Based Action Synthesis,
ICCV19(4393-4401)
IEEE DOI 2004
autoregressive processes, convolutional neural nets, Gaussian processes, graph theory, image motion analysis, Generative adversarial networks BibRef

Li, M.S.[Mao-Sen], Chen, S.H.[Si-Heng], Chen, X.[Xu], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng], Tian, Q.[Qi],
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition,
CVPR19(3590-3598).
IEEE DOI 2002
BibRef

Laraba, S.[Sohaib], Tilmanne, J.[JoŰlle], Dutoit, T.[Thierry],
Leveraging Pre-trained CNN Models for Skeleton-based Action Recognition,
CVS19(612-626).
Springer DOI 1912
BibRef

Khamsehashari, R., Gadzicki, K., Zetzsche, C.,
Deep Residual Temporal Convolutional Networks for Skeleton-based Human Action Recognition,
CVS19(376-385).
Springer DOI 1912
BibRef

Ye, F., Tang, H., Wang, X., Liang, X.,
Joints Relation Inference Network for Skeleton-Based Action Recognition,
ICIP19(16-20)
IEEE DOI 1910
Action Recognition, Relation Inference, Graph Convolutional Network, Skeleton BibRef

Rhif, M., Wannous, H., Farah, I.R.,
Action Recognition from 3D Skeleton Sequences using Deep Networks on Lie Group Features,
ICPR18(3427-3432)
IEEE DOI 1812
Feature extraction, Skeleton, Mathematical model, Tensile stress, Manifolds, Convolution BibRef

Noori, F.M.[Farzan Majeed], Wallace, B.[Benedikte], Uddin, M.Z.[M. Zia], Torresen, J.[Jim],
A Robust Human Activity Recognition Approach Using OpenPose, Motion Features, and Deep Recurrent Neural Network,
SCIA19(299-310).
Springer DOI 1906
BibRef

Uddin, M.Z.[M. Zia], Khaksar, W., Torresen, J.[Jim],
Activity Recognition Using Deep Recurrent Neural Network on Translation and Scale-Invariant Features,
ICIP18(475-479)
IEEE DOI 1809
Depth videos, segmentation, skeleton, Radon, RNN BibRef

Wang, B., Huang, L., Hoai, M.,
Active Vision for Early Recognition of Human Actions,
CVPR20(1078-1088)
IEEE DOI 2008
Cameras, Bandwidth, Learning (artificial intelligence), Robot sensing systems, Pattern recognition, Recurrent neural networks BibRef

Wang, B., Hoai, M.,
Predicting Body Movement and Recognizing Actions: An Integrated Framework for Mutual Benefits,
FG18(341-348)
IEEE DOI 1806
Dynamics, Forecasting, Recurrent neural networks, Robots, Skeleton, Trajectory, action early recognition, early detection BibRef

Wei, S.H.[Sheng-Hua], Song, Y.H.[Yong-Hong], Zhang, Y.L.[Yuan-Lin],
Human skeleton tree recurrent neural network with joint relative motion feature for skeleton based action recognition,
ICIP17(91-95)
IEEE DOI 1803
Acceleration, Feature extraction, Logic gates, Neurons, Recurrent neural networks, Shoulder, Skeleton, Action recognition, skeleton joints BibRef

Huang, Z., Wan, C., Probst, T., Van Gool, L.J.[Luc J.],
Deep Learning on Lie Groups for Skeleton-Based Action Recognition,
CVPR17(1243-1252)
IEEE DOI 1711
Machine learning, Manifolds, Neural networks, Skeleton, Transforms BibRef

Mavroudi, E.[Effrosyni], Bindal, P.[Prashast], Vidal, R.[RenÚ],
Actor-Centric Tubelets for Real-Time Activity Detection in Extended Videos,
Activity22(172-181)
IEEE DOI 2202
Visualization, Tracking, Surveillance, Focusing, Object detection, Real-time systems, Graph neural networks BibRef

Mavroudi, E., Tao, L., Vidal, R.,
Deep Moving Poselets for Video Based Action Recognition,
WACV17(111-120)
IEEE DOI 1609
BibRef
Earlier: A2, A3, Only:
Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition,
ChaLearnDec15(303-311)
IEEE DOI 1602
Feature extraction, Hip, Legged locomotion, Shoulder, Support vector machines, Trajectory, Computational modeling BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Articulatd Action Recognition .


Last update:Jul 18, 2024 at 20:50:34