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IEEE DOI
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BibRef
Yang, M.[Ming],
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Human action detection by boosting efficient motion features,
ObjectEvent09(522-529).
IEEE DOI
0910
BibRef
Yang, M.[Ming],
Lv, F.J.[Feng-Jun],
Xu, W.[Wei],
Gong, Y.H.[Yi-Hong],
Detection Driven Adaptive Multi-cue Integration for Multiple Human
Tracking,
ICCV09(1554-1561).
IEEE DOI
0909
BibRef
Ijjina, E.P.[Earnest Paul],
Chalavadi, K.M.[Krishna Mohan],
Human action recognition using genetic algorithms and convolutional
neural networks,
PR(59), No. 1, 2016, pp. 199-212.
Elsevier DOI
1609
Convolutional neural network (CNN)
BibRef
Earlier:
Human action recognition based on motion capture information using
fuzzy convolution neural networks,
ICAPR15(1-6)
IEEE DOI
1511
BibRef
Earlier:
Human Action Recognition Using Action Bank Features and Convolutional
Neural Networks,
DeepLearnV14(328-339).
Springer DOI
1504
convolution
BibRef
Ijjina, E.P.[Earnest Paul],
Chalavadi, K.M.[Krishna Mohan],
Human action recognition in RGB-D videos using motion sequence
information and deep learning,
PR(72), No. 1, 2017, pp. 504-516.
Elsevier DOI
1708
Multi-modal, action, recognition
BibRef
Liu, C.H.[Cai-Hua],
Liu, J.[Jie],
He, Z.C.[Zhi-Cheng],
Zhai, Y.J.[Yu-Jia],
Hu, Q.H.[Qing-Hua],
Huang, Y.[Yalou],
Convolutional neural random fields for action recognition,
PR(59), No. 1, 2016, pp. 213-224.
Elsevier DOI
1609
Action recognition
BibRef
Lei, J.,
Li, G.H.[Guo-Hui],
Zhang, J.,
Guo, Q.,
Tu, D.[Dan],
Continuous action segmentation and recognition using hybrid
convolutional neural network-hidden Markov model model,
IET-CV(10), No. 6, 2016, pp. 537-544.
DOI Link
1609
Gaussian processes
BibRef
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Cheng, Y.[Yun],
Xie, L.[Li],
Li, S.Z.[Shao-Zi],
Fully convolutional networks for action recognition,
IET-CV(11), No. 8, December 2017, pp. 744-749.
DOI Link
1712
BibRef
Shi, Y.M.[Ye-Min],
Tian, Y.H.[Yong-Hong],
Wang, Y.W.[Yao-Wei],
Huang, T.J.[Tie-Jun],
Sequential Deep Trajectory Descriptor for Action Recognition With
Three-Stream CNN,
MultMed(19), No. 7, July 2017, pp. 1510-1520.
IEEE DOI
1706
Cameras, Feature extraction, Histograms, Neural networks,
Optical imaging, Streaming media, Trajectory, Action recognition,
long-term motion, sequential deep trajectory descriptor (sDTD),
three-stream, framework
BibRef
Shi, Y.M.[Ye-Min],
Tian, Y.H.[Yong-Hong],
Wang, Y.W.[Yao-Wei],
Zeng, W.,
Huang, T.J.[Tie-Jun],
Learning Long-Term Dependencies for Action Recognition with a
Biologically-Inspired Deep Network,
ICCV17(716-725)
IEEE DOI
1802
cognitive systems, image recognition, image representation,
learning (artificial intelligence), neural nets,
Visualization
BibRef
Wang, P.[Peng],
Cao, Y.Z.H.[Yuan-Zhou-Han],
Shen, C.H.[Chun-Hua],
Liu, L.Q.[Ling-Qiao],
Shen, H.T.[Heng Tao],
Temporal Pyramid Pooling-Based Convolutional Neural Network for
Action Recognition,
CirSysVideo(27), No. 12, December 2017, pp. 2613-2622.
IEEE DOI
1712
Action recognition, convolutional neural network (CNN),
temporal pyramid pooling
BibRef
Wang, P.[Peng],
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
Shen, H.T.[Heng Tao],
Order-aware convolutional pooling for video based action recognition,
PR(91), 2019, pp. 357-365.
Elsevier DOI
1904
Action recognition, Convolutional neural network, Temporal pooling
BibRef
Wang, L.L.[Liang-Liang],
Ge, L.Z.[Lian-Zheng],
Li, R.F.[Rui-Feng],
Fang, Y.J.[Ya-Jun],
Three-stream CNNs for action recognition,
PRL(92), No. 1, 2017, pp. 33-40.
Elsevier DOI
1705
Action, recognition
BibRef
Wang, X.H.[Xuan-Han],
Gao, L.L.[Lian-Li],
Song, J.K.[Jing-Kuan],
Shen, H.T.[Heng-Tao],
Beyond Frame-level CNN:
Saliency-Aware 3-D CNN With LSTM for Video Action Recognition,
SPLetters(24), No. 4, April 2017, pp. 510-514.
IEEE DOI
1704
Computer architecture
BibRef
Wang, X.,
Gao, L.,
Wang, P.,
Sun, X.,
Liu, X.,
Two-Stream 3-D convNet Fusion for Action Recognition in Videos With
Arbitrary Size and Length,
MultMed(20), No. 3, March 2018, pp. 634-644.
IEEE DOI
1802
Computational modeling, Convolution, Feature extraction,
Histograms,
Action recognition
BibRef
Zhang, B.W.[Bo-Wen],
Wang, L.M.[Li-Min],
Wang, Z.[Zhe],
Qiao, Y.[Yu],
Wang, H.L.[Han-Li],
Real-Time Action Recognition With Deeply Transferred Motion Vector
CNNs,
IP(27), No. 5, May 2018, pp. 2326-2339.
IEEE DOI
1804
BibRef
Earlier:
Real-Time Action Recognition with Enhanced Motion Vector CNNs,
CVPR16(2718-2726)
IEEE DOI
1612
convolution, feature extraction, feedforward neural nets,
image motion analysis, image sequences, video signal processing,
real-time processing
BibRef
Jing, L.L.[Long-Long],
Yang, X.D.[Xiao-Dong],
Tian, Y.L.[Ying-Li],
Video you only look once: Overall temporal convolutions for action
recognition,
JVCIR(52), 2018, pp. 58-65.
Elsevier DOI
1804
Video understanding, Video classification, Action recognition,
Convolutional neural network
BibRef
Zhao, S.C.[Shi-Chao],
Liu, Y.B.[Yan-Bin],
Han, Y.H.[Ya-Hong],
Hong, R.C.[Ri-Chang],
Hu, Q.H.[Qing-Hua],
Tian, Q.[Qi],
Pooling the Convolutional Layers in Deep ConvNets for Video Action
Recognition,
CirSysVideo(28), No. 8, August 2018, pp. 1839-1849.
IEEE DOI
1808
Feature extraction, Trajectory, Convolutional codes,
Image recognition, Optical imaging, Encoding, Histograms, ConvNets,
feature fusion
BibRef
Miao, N.Y.[Nai-Yang],
Xue, F.[Feng],
Hong, R.C.[Ri-Chang],
Multimodal Semantics-Based Supervised Latent Dirichlet Allocation for
Event Classification,
MultMedMag(28), No. 4, October 2021, pp. 8-17.
IEEE DOI
2112
Semantics, Visualization, Social networking (online), Data models,
Feature extraction, Dictionaries, Data mining,
Supervised LDA
BibRef
Yang, H.[Hao],
Yuan, C.F.[Chun-Feng],
Li, B.[Bing],
Du, Y.[Yang],
Xing, J.L.[Jun-Liang],
Hu, W.M.[Wei-Ming],
Maybank, S.J.[Stephen J.],
Asymmetric 3D Convolutional Neural Networks for action recognition,
PR(85), 2019, pp. 1-12.
Elsevier DOI
1810
Asymmetric 3D convolution, MicroNets, 3D-CNN, Action recognition
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Zhuang, Z.D.[Zhen-Dong],
Xue, Y.[Yang],
TS-ICNN: Time Sequence-Based Interval Convolutional Neural Networks for
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IEICE(E101-D), No. 10, October 2018, pp. 2534-2538.
WWW Link.
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BibRef
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Yao, R.[Rui],
Lin, G.S.[Guo-Sheng],
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recognition,
IVC(81), 2019, pp. 34-41.
Elsevier DOI
1902
Action recognition, Residual connection, Local fusion,
Deep convolutional network
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Zalluhoglu, C.[Cemil],
Ikizler-Cinbis, N.[Nazli],
Region based multi-stream convolutional neural networks for
collective activity recognition,
JVCIR(60), 2019, pp. 170-179.
Elsevier DOI
1903
Collective activity recognition, Action recognition
BibRef
Tanisik, G.[Gokhan],
Zalluhoglu, C.[Cemil],
Ikizler-Cinbis, N.[Nazli],
Multi-stream pose convolutional neural networks for human interaction
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SP:IC(95), 2021, pp. 116265.
Elsevier DOI
2106
Human-human interactions, Convolutional neural networks, Poses
BibRef
Yang, K.[Ke],
Shen, X.L.[Xiao-Long],
Qiao, P.[Peng],
Li, S.J.[Shi-Jie],
Li, D.S.[Dong-Sheng],
Dou, Y.[Yong],
Exploring frame segmentation networks for temporal action
localization,
JVCIR(61), 2019, pp. 296-302.
Elsevier DOI
1906
Temporal CNN after 2D spatial CNN.
Action detection, Temporal action localization, Convolutional Neural Network
BibRef
Li, C.,
Zhang, B.,
Chen, C.,
Ye, Q.,
Han, J.,
Guo, G.,
Ji, R.,
Deep Manifold Structure Transfer for Action Recognition,
IP(28), No. 9, Sep. 2019, pp. 4646-4658.
IEEE DOI
1908
backpropagation, convolutional neural nets, feature extraction,
image recognition, image representation, minimisation,
ADMM-BP
BibRef
Purwanto, D.,
Pramono, R.R.A.,
Chen, Y.T.,
Fang, W.H.,
Three-Stream Network With Bidirectional Self-Attention for Action
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SPLetters(26), No. 8, August 2019, pp. 1187-1191.
IEEE DOI
1908
BibRef
And:
Corrections:
SPLetters(27), 2020, pp. 2188-2188.
IEEE DOI
2012
feature extraction, image motion analysis, image recognition,
image representation, image resolution, neural nets,
deep learning
BibRef
Pramono, R.R.A.[Rizard Renanda Adhi],
Chen, Y.T.[Yie-Tarng],
Fang, W.H.[Wen-Hsien],
Spatial-Temporal Action Localization With Hierarchical Self-Attention,
MultMed(24), 2022, pp. 625-639.
IEEE DOI
2202
Location awareness, Electron tubes, Videos, Proposals,
Object detection, Detectors, Action localization, self-attention,
data association
BibRef
Purwanto, D.,
Pramono, R.R.A.[R. Renanda Adhi],
Chen, Y.T.[Yie-Tarng],
Fang, W.H.[Wen-Hsien],
Extreme Low Resolution Action Recognition with Spatial-Temporal
Multi-Head Self-Attention and Knowledge Distillation,
RLQ19(961-969)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image motion analysis, image recognition, image resolution,
self attention
BibRef
Li, Y.G.[Yong-Gang],
Ge, R.[Rui],
Ji, Y.[Yi],
Gong, S.R.[Sheng-Rong],
Liu, C.P.[Chun-Ping],
Trajectory-Pooled Spatial-Temporal Architecture of Deep Convolutional
Neural Networks for Video Event Detection,
CirSysVideo(29), No. 9, September 2019, pp. 2683-2692.
IEEE DOI
1909
Trajectory, Event detection, Feature extraction,
Image motion analysis, Fuses,
deep feature
BibRef
Wang, L.M.[Li-Min],
Xiong, Y.J.[Yuan-Jun],
Wang, Z.[Zhe],
Qiao, Y.[Yu],
Lin, D.[Dahua],
Tang, X.[Xiaoou],
Van Gool, L.J.[Luc J.],
Temporal Segment Networks for Action Recognition in Videos,
PAMI(41), No. 11, November 2019, pp. 2740-2755.
IEEE DOI
1910
Videos, Training, Adaptation models, Analytical models,
Visualization, Histograms, Action recognition,
ConvNets
BibRef
Duan, H.D.[Hao-Dong],
Zhao, Y.[Yue],
Chen, K.[Kai],
Xiong, Y.J.[Yuan-Jun],
Lin, D.[Dahua],
Mitigating Representation Bias in Action Recognition:
Algorithms and Benchmarks,
CVEU22(557-575).
Springer DOI
2304
BibRef
Wang, L.M.[Li-Min],
Qiao, Y.[Yu],
Tang, X.[Xiaoou],
Van Gool, L.J.[Luc J.],
Actionness Estimation Using Hybrid Fully Convolutional Networks,
CVPR16(2708-2717)
IEEE DOI
1612
BibRef
Lee, D.G.[Dong-Gyu],
Lee, S.W.[Seong-Whan],
Prediction of partially observed human activity based on pre-trained
deep representation,
PR(85), 2019, pp. 198-206.
Elsevier DOI
1810
Pre-trained CNN, Human activity prediction, Human interaction,
Sub-volume co-occurrence matrix
BibRef
Soltanian, M.,
Ghaemmaghami, S.,
Hierarchical Concept Score Postprocessing and Concept-Wise
Normalization in CNN-Based Video Event Recognition,
MultMed(21), No. 1, January 2019, pp. 157-172.
IEEE DOI
1901
Task analysis, Visualization, Event detection, Training,
Feature extraction, Support vector machines, Semantics,
mean average precision
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Zhu, S.[Suguo],
Fang, Z.Y.[Zhen-Ying],
Wang, Y.[Yi],
Yu, J.[Jun],
Du, J.P.[Jun-Ping],
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Elsevier DOI
1903
Activity recognition, Multimodal, Visual attention
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DOI Link
1904
BibRef
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Xie, W.,
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Li, B.,
Yuan, J.,
Semantic Cues Enhanced Multimodality Multistream CNN for Action
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CirSysVideo(29), No. 5, May 2019, pp. 1423-1437.
IEEE DOI
1905
Semantics, Feature extraction, Spatiotemporal phenomena,
Visualization, Object segmentation, Object detection,
multi-modalities
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A weakly supervised CNN model for spatial localization of human
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PR(98), 2020, pp. 107037.
Elsevier DOI
1911
Action recognition, Spatio-temporal deformable,
Attention mechanism, 3D ConvNets
BibRef
Li, Z.L.[Zhi-Lei],
Li, J.[Jun],
Ma, Y.Q.[Yu-Qing],
Wang, R.[Rui],
Shi, Z.P.[Zhi-Ping],
Ding, Y.[Yifu],
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Spatio-Temporal Adaptive Network With Bidirectional Temporal
Difference for Action Recognition,
CirSysVideo(33), No. 9, September 2023, pp. 5174-5185.
IEEE DOI
2310
BibRef
Li, J.[Jun],
Liu, X.L.[Xiang-Long],
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Song, J.K.[Jing-Kuan],
Sebe, N.[Nicu],
Spatio-Temporal Attention Networks for Action Recognition and
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MultMed(22), No. 11, November 2020, pp. 2990-3001.
IEEE DOI
2010
Feature extraction, Task analysis,
Optical imaging,
action detection
BibRef
Agethen, S.[Sebastian],
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Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based
Mechanism for Videos,
MultMed(22), No. 3, March 2020, pp. 819-829.
IEEE DOI
2003
Kernel, Videos, Task analysis, Convolution, Feature extraction,
YouTube, Mathematical model, recurrent neural networks
BibRef
Li, X.Q.[Xiao-Qiang],
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Ding, G.T.[Guang-Tai],
Tong, W.Q.[Wei-Qin],
Dual attention convolutional network for action recognition,
IET-IPR(14), No. 6, 11 May 2020, pp. 1059-1065.
DOI Link
2005
BibRef
Atto, A.M.[Abdourrahmane Mahamane],
Benoit, A.[Alexandre],
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Timed-image based deep learning for action recognition in video
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PR(104), 2020, pp. 107353.
Elsevier DOI
2005
Data conditioning, Video analysis, Deep learning,
Convolution frames, Hilbert space-filling curve, Violence detection
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Yoon, D.H.[Da-Hye],
Cho, N.G.[Nam-Gyu],
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A novel online action detection framework from untrimmed video
streams,
PR(106), 2020, pp. 107396.
Elsevier DOI
2006
Online action detection, Untrimmed video stream,
Future frame generation, 3D convolutional neural network,
Long short-term memory
BibRef
Kim, H.J.[Ho-Joong],
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Kong, H.[Heejo],
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TE-TAD: Towards Full End-to-End Temporal Action Detection via
Time-Aligned Coordinate Expression,
CVPR24(18837-18846)
IEEE DOI Code:
WWW Link.
2410
Adaptation models, Codes, Computational modeling, Detectors,
Object detection, Benchmark testing, temporal action detection,
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Mishra, T.K.[Tusar Kanti],
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Elsevier DOI
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Frame extraction, Human action recognition, CNN Frame extraction, CNN
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Convolutional Networks With Channel and STIPs Attention Model for
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IEEE DOI
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Videos, Dynamics, Feature extraction,
Image sequences, Convolution, deep networks
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Xiao, J.[Jihai],
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Human action recognition based on convolutional neural network and
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Elsevier DOI
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Human action recognition, Spatial pyramid,
Convolution neural networks, Cosine distance measure
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Fu, B.[Bo],
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Deep Residual Split Directed Graph Convolutional Neural Networks for
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IEEE DOI
2012
Neural networks, Convolutional neural networks, Training,
Data mining, Feature extraction,
Residual-split block
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Multi-frame based adversarial learning approach for video
surveillance,
PR(122), 2022, pp. 108350.
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2112
Temporal sampling, Multi-scale adversarial learning,
Foreground-background segmentation and video surveillance
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A Deep Reinforcement Learning Method For Multimodal Data Fusion in
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SPLetters(29), 2022, pp. 120-124.
IEEE DOI
2202
Reinforcement learning, Resource management, Data models,
Signal processing algorithms, Task analysis, Neural networks,
deep reinforcement learning
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Yang, Z.Q.[Zhao-Qilin],
An, G.Y.[Gao-Yun],
Zhang, R.C.[Rui-Chen],
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SRI3D: Two-stream inflated 3D ConvNet based on sparse regularization
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IET-IPR(17), No. 5, 2023, pp. 1438-1448.
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2304
convolutional neural nets, neural nets, video signal processing
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2305
Human action recognition, 2D CNNs, Channel attention, Spatiotemporal modeling
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Li, S.J.[Shi-Jie],
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Liu, Y.[Yun],
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MS-TCN++: Multi-Stage Temporal Convolutional Network for Action
Segmentation,
PAMI(45), No. 6, June 2023, pp. 6647-6658.
IEEE DOI
2305
Videos, Hidden Markov models, Convolution, Task analysis,
Adaptation models, Temporal action segmentation, temporal convolutional network
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Farha, Y.A.[Yazan Abu],
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MS-TCN: Multi-Stage Temporal Convolutional Network for Action
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IEEE DOI
2002
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Farha, Y.A.[Yazan Abu],
Ke, Q.H.[Qiu-Hong],
Schiele, B.[Bernt],
Gall, J.[Juergen],
Long-Term Anticipation of Activities with Cycle Consistency,
GCPR20(159-173).
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Chen, Z.Z.[Zhen-Zhong],
3D Deformable Convolution Temporal Reasoning network for action
recognition,
JVCIR(93), 2023, pp. 103804.
Elsevier DOI
2305
Action recognition, 3D deformable convolutional network, Reasoning
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Sudhakaran, S.[Swathikiran],
Escalera, S.[Sergio],
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Gate-Shift-Fuse for Video Action Recognition,
PAMI(45), No. 9, September 2023, pp. 10913-10928.
IEEE DOI
2309
BibRef
Earlier:
Gate-Shift Networks for Video Action Recognition,
CVPR20(1099-1108)
IEEE DOI
2008
GSM, Convolution, Feature extraction, Logic gates, Kernel
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Yu, C.Z.[Cheng-Zhang],
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DenseGCN: A multi-level and multi-temporal graph convolutional
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DOI Link
2310
data analysis, pattern recognition
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Wang, H.[Hao],
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LGANet: Local and global attention are both you need for action
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IET-IPR(17), No. 12, 2023, pp. 3453-3463.
DOI Link
2310
Action recognition, Convolutional neural networks, Efficient,
Transformer, Video understanding
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Kim, M.J.[Min-Ji],
Han, D.Y.[Dong-Yoon],
Kim, T.[Taekyung],
Han, B.H.[Bo-Hyung],
Leveraging Temporal Contextualization for Video Action Recognition,
ECCV24(XXI: 74-91).
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2412
BibRef
Seon, J.[Jonghyeon],
Hwang, J.[Jaedong],
Mun, J.[Jonghwan],
Han, B.H.[Bo-Hyung],
Stop or Forward: Dynamic Layer Skipping for Efficient Action
Recognition,
WACV23(3350-3359)
IEEE DOI
2302
Location awareness, Knowledge engineering, Heuristic algorithms,
Semantics, Memory management, Termination of employment
BibRef
Zhang, Y.K.[Yong-Kang],
Zhang, H.[Han],
Wu, G.M.[Guo-Ming],
Xu, Y.F.[Yang-Fan],
Shi, Z.P.[Zhi-Ping],
Li, J.[Jun],
TMN: Temporal-guided Multiattention Network for Action Recognition,
ICPR22(2964-2970)
IEEE DOI
2212
Adaptive systems, Navigation, Fuses, Aggregates, Benchmark testing,
Excavation, Spatiotemporal phenomena
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Teepe, T.[Torben],
Khan, A.[Ali],
Gilg, J.[Johannes],
Herzog, F.[Fabian],
Hörmann, S.[Stefan],
Rigoll, G.[Gerhard],
Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait
Recognition,
ICIP21(2314-2318)
IEEE DOI
2201
Convolutional codes, Visualization, Image recognition,
Pose estimation, Neural networks, Feature extraction, Skeleton,
Graph Neural Networks
BibRef
Hong, S.G.[Seul-Gi],
Choi, M.K.[Min-Kook],
Blockwise Temporal-Spatial Pathway Network,
ICIP21(3677-3681)
IEEE DOI
2201
Adaptation models, Visualization, Image recognition, Image coding,
Fuses, Feature extraction, Temporal-Spatial Representation,
3DCNN, Action Recognition
BibRef
Alijanpour, M.[Mohammad],
Raie, A.[Abolghasem],
Video Event Recognition using Two-Stream Convolutional Neural
Networks,
IPRIA21(1-5)
IEEE DOI
2201
Image recognition, Image analysis, Motion estimation, Estimation,
Streaming media, Feature extraction, Complexity theory,
Frame Subtraction
BibRef
Chen, C.F.R.[Chun-Fu Richard],
Panda, R.[Rameswar],
Ramakrishnan, K.[Kandan],
Feris, R.S.[Rogerio S.],
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IVCNZ20(1-6)
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Deep learning, Image recognition,
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ICIP20(1801-1805)
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Videos, Kernel, Convolution, Solid modeling, Action Recognition,
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ICIP20(1391-1395)
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Proposals, Feature extraction, Motion segmentation,
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ICIP19(300-304)
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Human action detection, pseudo-3D con-volutional tube network, tube proposal
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Deep Learning, Optical Flow, Data Normalization,
Action classification, Spatio-temporal convolution
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ICIP19(1585-1589)
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Action segmentation, multiscale modeling,
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ICIP19(21-25)
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Time-asymmetric 3D Convolution, 3D CNN, Action Recognition
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WACV19(71-80)
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convolutional neural nets, entropy, feature extraction,
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WACV19(51-60)
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convolutional neural nets, image motion analysis,
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Computer architecture
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ICIP18(3468-3472)
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1711
Aggregates, Convolution, Feature extraction,
Image recognition, Streaming media, Trajectory
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Wang, Y.,
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CVPR17(2097-2106)
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1711
Convolution, Optical imaging, Optical losses,
Spatiotemporal phenomena, Training
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AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks
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CVPR17(5699-5708)
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1711
Feature extraction, Hidden Markov models, Prediction algorithms,
Standards, Training, Videos
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ChaLearn15(10-14)
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1510
Convolutional codes
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ICCV15(4041-4049)
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LSTM: long short-term memory Neural Network.
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FG17(526-531)
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1707
Activity recognition, Algorithm design and analysis,
Logic gates, Neural networks, Trajectory,
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CVPR16(1933-1941)
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Human Performance Capture from Monocular Video in the Wild,
3DV21(889-898)
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2201
Learning systems, Human computer interaction,
Surface reconstruction, Solid modeling, Shape, Imaging, 3D Human,
Human Performance Capture
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WACV16(1-8)
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Zero-Shot, One-Shot, Few-Shot Learning for Human Action Recognition .