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.,
Rodolà, 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.[Baodi],
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
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
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
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
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
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.,
Alenyà, 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.[Joëlle],
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.[René],
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 .