17.1.3.7.7 Neural Networks and Learning for Human Action Recognition and Detection

Nascimento, J.C.[Jacinto C.], Figueiredo, M.A.T.[Mario A.T.], Marques, J.S.[Jorge S.],
Independent increment processes for human motion recognition,
CVIU(109), No. 2, February 2008, pp. 126-138.
Elsevier DOI 0711
BibRef
Earlier:
Semi-Supervised Learning of Switched Dynamical Models for Classification of Human Activities in Surveillance Applications,
ICIP07(III: 197-200).
IEEE DOI 0709
BibRef
Earlier:
On-Line Classification of Human Activities,
IbPRIA07(II: 444-451).
Springer DOI 0706
BibRef
Earlier:
Recognition of Human Activities Using Space Dependent Switched Dynamical Models,
ICIP05(III: 852-855).
IEEE DOI 0512
Surveillance; Human motion; Activity recognition; Independent increment processes; Minimum description length BibRef

Nascimento, J.C.[Jacinto C.], Marques, J.S.[Jorge S.], Figueiredo, M.A.T.[Mario A. T.],
Discriminative model selection using a modified Bayesian criterion: Application to trajectory modeling,
ICIP11(1429-1432).
IEEE DOI 1201
BibRef
Earlier:
Classification of complex pedestrian activities from trajectories,
ICIP10(3481-3484).
IEEE DOI 1009
BibRef
And:
Discriminative model selection for object motion recognition,
ICIP10(3953-3956).
IEEE DOI 1009
BibRef

Nascimento, J.C.[Jacinto C.], Figueiredo, M.A.T.[Mario A.T.], Marques, J.S.[Jorge S.],
Unsupervised learning of motion patterns using generative models,
ICIP08(761-764).
IEEE DOI 0810
BibRef

Escobar, M.J.[Maria-Jose], Masson, G.S.[Guillaume S.], Vieville, T.[Thierry], Kornprobst, P.[Pierre],
Action Recognition Using a Bio-Inspired Feedforward Spiking Network,
IJCV(82), No. 3, May 2009, pp. xx-yy.
Springer DOI 0903
BibRef

Medathati, N.V.K.[N. V. Kartheek], Neumann, H.[Heiko], Masson, G.S.[Guillaume S.], Kornprobst, P.[Pierre],
Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision,
CVIU(150), No. 1, 2016, pp. 1-30.
Elsevier DOI 1608
Canonical computations BibRef

Escobar, M.J.[María-José], Kornprobst, P.[Pierre],
Action recognition via bio-inspired features: The richness of center-surround interaction,
CVIU(116), No. 5, May 2012, pp. 593-605.
Elsevier DOI 1203
BibRef
Earlier:
Action Recognition with a Bio-inspired Feedforward Motion Processing Model: The Richness of Center-Surround Interactions,
ECCV08(IV: 186-199).
Springer DOI 0810
Action recognition; Bio-inspired models; V1; MT; Motion analysis; Center-surround interaction BibRef

Liang, Y.M.[Yu-Ming], Shih, S.W.[Sheng-Wen], Shih, C.C.[Chun-Chieh], Liao, H.Y.M., Lin, C.C.[Cheng-Chung],
Learning Atomic Human Actions Using Variable-Length Markov Models,
SMC-B(39), No. 1, February 2009, pp. 268-280.
IEEE DOI 0902
BibRef

Patricio, M.A.[Miguel A.], García, J.[Jesús], Berlanga, A.[Antonio], Molina, J.M.[José M.],
Visual data association for real-time video tracking using genetic and estimation of distribution algorithms,
IJIST(19), No. 3, September 2009, pp. 208-220.
DOI Link 0909
BibRef

Loza, A., Patricio, M.A.[Miguel A.], García, J.[Jesús], Molina, J.M.[José M.],
Advanced algorithms for real-time video tracking with multiple targets,
ICARCV08(125-131).
IEEE DOI 1109
BibRef

Perez Concha, O.[Oscar], Xu, R.Y.D.[Richard Yi Da], Piccardi, M.[Massimo],
Robust Dimensionality Reduction for Human Action Recognition,
DICTA10(349-356).
IEEE DOI 1012
BibRef

Pérez, Ó.[Óscar], Piccardi, M.[Massimo], García, J.[Jesús], Patricio, M.Á.[Miguel Ángel], Molina, J.M.[José Manuel],
Comparison Between Genetic Algorithms and the Baum-Welch Algorithm in Learning HMMs for Human Activity Classification,
EvoIASP07(399-406).
Springer DOI 0704
BibRef

Piccardi, M.[Massimo], Perez, O.[Oscar],
Hidden Markov Models with Kernel Density Estimation of Emission Probabilities and their Use in Activity Recognition,
VS07(1-8).
IEEE DOI 0706
BibRef

Liu, C.[Chang], Yuen, P.C.[Pong C.],
Human action recognition using boosted EigenActions,
IVC(28), No. 5, May 2010, pp. 825-835.
Elsevier DOI 1003
BibRef
Earlier:
Boosting EigenActions: A new algorithm for human action categorization,
FG08(1-6).
IEEE DOI 0809
Human action recognition; Salient action unit; Adaboost BibRef

Liu, C.[Chang], Yuen, P.C.[Pong C.],
A Boosted Co-Training Algorithm for Human Action Recognition,
CirSysVideo(21), No. 9, September 2011, pp. 1203-1213.
IEEE DOI 1109
BibRef

Han, L.[Lei], Wu, X.X.[Xin-Xiao], Liang, W.[Wei], Hou, G.M.[Guang-Ming], Jia, Y.D.[Yun-De],
Discriminative human action recognition in the learned hierarchical manifold space,
IVC(28), No. 5, May 2010, pp. 836-849.
Elsevier DOI 1003
BibRef
Earlier: A1, A3, A2, A5, Only:
Human action recognition using discriminative models in the learned hierarchical manifold space,
FG08(1-6).
IEEE DOI 0809
Human action recognition; Discriminative model; Hierarchical manifold learning; Mutual invariant; Motion pattern
See also Action recognition feedback-based framework for human pose reconstruction from monocular images. BibRef

Song, Y.[Yan], Zheng, Y.T.[Yan-Tao], Tang, S.[Sheng], Zhou, X.D.[Xiang-Dong], Zhang, Y.D.[Yong-Dong], Lin, S.X.[Shou-Xun], Chua, T.S.[Tat-Seng],
Localized Multiple Kernel Learning for Realistic Human Action Recognition in Videos,
CirSysVideo(21), No. 9, September 2011, pp. 1193-1202.
IEEE DOI 1109
BibRef

Liu, A.A.[An-An], Xu, N.[Ning], Nie, W.Z.[Wei-Zhi], Su, Y.T.[Yu-Ting], Zhang, Y.D.[Yong-Dong],
Multi-Domain and Multi-Task Learning for Human Action Recognition,
IP(28), No. 2, February 2019, pp. 853-867.
IEEE DOI 1811
Task analysis, Training data, Feature extraction, Visualization, Data mining, Data models, human action recognition
See also Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition.
See also Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition. BibRef

Seo, H.J.[Hae Jong], Milanfar, P.[Peyman],
Action Recognition from One Example,
PAMI(33), No. 1, January 2011, pp. 867-882.
IEEE DOI 1104
BibRef
Earlier:
Detection of human actions from a single example,
ICCV09(1965-1970).
IEEE DOI 0909
Single example as query to find others BibRef

Gilbert, A.[Andrew], Illingworth, J.[John], Bowden, R.[Richard],
Action Recognition Using Mined Hierarchical Compound Features,
PAMI(33), No. 1, January 2011, pp. 883-897.
IEEE DOI 1104
BibRef
Earlier:
Fast realistic multi-action recognition using mined dense spatio-temporal features,
ICCV09(925-931).
IEEE DOI 0909
BibRef
Earlier:
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners,
ECCV08(I: 222-233).
Springer DOI 0810
Start from basic feature points as in 2D recognition. BibRef

Gilbert, A.[Andrew], Bowden, R.[Richard],
Data Mining for Action Recognition,
ACCV14(V: 290-303).
Springer DOI 1504
BibRef

Oshin, O.[Olusegun], Gilbert, A.[Andrew], Bowden, R.[Richard],
Capturing relative motion and finding modes for action recognition in the wild,
CVIU(125), No. 1, 2014, pp. 155-171.
Elsevier DOI 1406
BibRef
Earlier:
There Is More Than One Way to Get Out of a Car: Automatic Mode Finding for Action Recognition in the Wild,
IbPRIA11(41-48).
Springer DOI 1106
BibRef
And:
Capturing the relative distribution of features for action recognition,
FG11(111-116).
IEEE DOI 1103
Action recognition BibRef

Oshin, O.[Olusegun], Gilbert, A.[Andrew], Illingworth, J.[John], Bowden, R.[Richard],
Action recognition using Randomised Ferns,
ObjectEvent09(530-537).
IEEE DOI 0910
BibRef
Earlier:
Spatio-temporal feature recognition using randomised Ferns,
MLMotion08(xx-yy). 0810
BibRef

Liu, J.G.[Jin-Gen], Yang, Y.[Yang], Saleemi, I.[Imran], Shah, M.[Mubarak],
Learning semantic features for action recognition via diffusion maps,
CVIU(116), No. 3, March 2012, pp. 361-377.
Elsevier DOI 1201
BibRef
Earlier: A1, A2, A4, Only:
Learning semantic visual vocabularies using diffusion distance,
CVPR09(461-468).
IEEE DOI 0906
Action recognition; Bag of video words; Semantic visual vocabulary; Diffusion Maps; Pointwise Mutual Information BibRef

Yang, Y.[Yang], Shah, M.[Mubarak],
Learning discriminative features and metrics for measuring action similarity,
ICIP14(1555-1559)
IEEE DOI 1502
Accuracy BibRef

Reddy, K.K.[Kishore K.], Liu, J.G.[Jin-Gen], Shah, M.[Mubarak],
Incremental action recognition using feature-tree,
ICCV09(1010-1017).
IEEE DOI 0909
BibRef

Liu, J.G.[Jin-Gen], Luo, J.B.[Jie-Bo], Shah, M.[Mubarak],
Recognizing realistic actions from videos 'in the wild',
CVPR09(1996-2003).
IEEE DOI 0906
BibRef

Liu, J.G.[Jin-Gen], Ali, S.[Saad], Shah, M.[Mubarak],
Recognizing human actions using multiple features,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Ali, S.[Saad], Basharat, A.[Arslan], Shah, M.[Mubarak],
Chaotic Invariants for Human Action Recognition,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Guha, T.[Tanaya], Ward, R.K.[Rabab K.],
Learning Sparse Representations for Human Action Recognition,
PAMI(34), No. 8, August 2012, pp. 1576-1588.
IEEE DOI 1206
BibRef
Earlier:
Action recognition by learnt class-specific overcomplete dictionaries,
FG11(143-148).
IEEE DOI 1103
sparse representation from dictionary in action recognition. Human movement, facial expressions. 3 frameworks. Each descriptor is linear combination of dictionary elements. BibRef

Guha, T.[Tanaya], Ward, R.K.[Rabab K.],
Image Similarity Using Sparse Representation and Compression Distance,
MultMed(16), No. 4, June 2014, pp. 980-987.
IEEE DOI 1407
Approximation methods BibRef

Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin],
Latent semantic learning with structured sparse representation for human action recognition,
PR(46), No. 7, July 2013, pp. 1799-1809.
Elsevier DOI 1303
Human action recognition; Latent semantic learning; Spectral embedding; Structured sparse representation; L 1 - norm hypergraph regularization BibRef

Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin], Ip, H.H.S.[Horace H.S.],
Spectral learning of latent semantics for action recognition,
ICCV11(1503-1510).
IEEE DOI 1201

See also Contextual Kernel and Spectral Methods for Learning the Semantics of Images. BibRef

Zhang, J.G.[Jian-Gen], Yao, B.[Benjamin], Wang, Y.T.[Yong-Tian],
Auto learning temporal atomic actions for activity classification,
PR(46), No. 7, July 2013, pp. 1789-1798.
Elsevier DOI 1303
Activity classification; Atomic action; Temporal-HDP BibRef

Wang, L.[Liang], Wang, Y.Z.[Yi-Zhou], Jiang, T.T.[Ting-Ting], Zhao, D.B.[De-Bin], Gao, W.[Wen],
Learning discriminative features for fast frame-based action recognition,
PR(46), No. 7, July 2013, pp. 1832-1840.
Elsevier DOI 1303
Frame-based action recognition; Feature mining BibRef

Wu, J.Z.[Jian-Zhai], Hu, D.[Dewen],
Learning Effective Event Models to Recognize a Large Number of Human Actions,
MultMed(16), No. 1, January 2014, pp. 147-158.
IEEE DOI 1402
image motion analysis BibRef

Chen, C.H.[Chang-Hong], Yang, S.Q.[Shun-Qing], Gan, Z.L.[Zong-Liang],
Topic-Based Knowledge Transfer Algorithm for Cross-View Action Recognition,
IEICE(E97-D), No. 3, March 2014, pp. 614-617.
WWW Link. 1403
BibRef

Luo, G.[Guan], Yang, S., Tian, G., Yuan, C.F.[Chun-Feng], Hu, W.M.[Wei-Ming], Maybank, S.J.[Steve J.],
Learning Human Actions by Combining Global Dynamics and Local Appearance,
PAMI(36), No. 12, December 2014, pp. 2466-2482.
IEEE DOI 1411
Behavioral science BibRef

Luo, G.[Guan], Wei, J.T.[Jiu-Tong], Hu, W.M.[Wei-Ming], Maybank, S.J.[Stephen J.],
Tangent Fisher Vector on Matrix Manifolds for Action Recognition,
IP(29), 2020, pp. 3052-3064.
IEEE DOI 2002
Manifolds, Video sequences, Observability, Videos, Covariance matrices, Kernel, Computational modeling, matrix manifold BibRef

Vrigkas, M.[Michalis], Karavasilis, V.[Vasileios], Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Matching mixtures of curves for human action recognition,
CVIU(119), No. 1, 2014, pp. 27-40.
Elsevier DOI 1402
Human action recognition
See also novel framework for motion segmentation and tracking by clustering incomplete trajectories, A. BibRef

Vrigkas, M.[Michalis], Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Identifying Human Behaviors Using Synchronized Audio-Visual Cues,
AffCom(8), No. 1, January 2017, pp. 54-66.
IEEE DOI 1703
BibRef
Earlier:
Active privileged learning of human activities from weakly labeled samples,
ICIP16(3036-3040)
IEEE DOI 1610
Computational modeling. Entropy BibRef

Vrigkas, M.[Michalis], Mastora, E.[Ermioni], Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Robust Incremental Hidden Conditional Random Fields for Human Action Recognition,
ISVC18(126-136).
Springer DOI 1811
BibRef

Vrigkas, M.[Michalis], Kazakos, E., Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Inferring Human Activities Using Robust Privileged Probabilistic Learning,
TASKCV17(2658-2665)
IEEE DOI 1802
Activity recognition, Probabilistic logic, Robustness, Testing, Training, Videos, Visualization BibRef

Chen, F.F.[Fei-Fei], Sang, N.[Nong], Kuang, X.Q.[Xiao-Qin], Gan, H.T.[Hai-Tao], Gao, C.X.[Chang-Xin],
Action recognition through discovering distinctive action parts,
JOSA-A(32), No. 2, February 2015, pp. 173-185.
DOI Link 1502
BibRef
Earlier: A1, A2, A5, A3, Only:
Discovering distinctive action parts for action recognition,
ICIP14(1520-1524)
IEEE DOI 1502
Pattern recognition. Computer vision BibRef

Zhang, S.W.[Shi-Wei], Gao, C.X.[Chang-Xin], Chen, F.F.[Fei-Fei], Luo, S.H.[Si-Hui], Sang, N.[Nong],
Group Sparse-Based Mid-Level Representation for Action Recognition,
SMCS(47), No. 4, April 2017, pp. 660-672.
IEEE DOI 1704
Detectors BibRef

Zhang, S.W.[Shi-Wei], Gao, C.X.[Chang-Xin], Zhang, J., Chen, F.F.[Fei-Fei], Sang, N.[Nong],
Discriminative Part Selection for Human Action Recognition,
MultMed(20), No. 4, April 2018, pp. 769-780.
IEEE DOI 1804
Correlation, Feature extraction, Solid modeling, Support vector machines, Training, Trajectory, Videos, Mid-level, recursive part elimination (RPE) BibRef

Zhou, Z., Shi, F., Wu, W.,
Learning Spatial and Temporal Extents of Human Actions for Action Detection,
MultMed(17), No. 4, April 2015, pp. 512-525.
IEEE DOI 1503
Discrete cosine transforms BibRef

Feng, Y.[Yang], Wu, X.X.[Xin-Xiao], Jia, Y.,
Multi-group-multi-class domain adaptation for event recognition,
IET-CV(10), No. 1, 2016, pp. 60-66.
DOI Link 1601
neural nets BibRef

Feng, Y.[Yang], Wu, X.X.[Xin-Xiao], Wang, H.[Han], Liu, J.[Jing],
Multi-group Adaptation for Event Recognition from Videos,
ICPR14(3915-3920)
IEEE DOI 1412
Events in consumer videos. Based on loosely labeld web videos. BibRef

Qin, J., Liu, L., Zhang, Z., Wang, Y., Shao, L.,
Compressive Sequential Learning for Action Similarity Labeling,
IP(25), No. 2, February 2016, pp. 756-769.
IEEE DOI 1601
Boosting BibRef

Zhang, J.G.[Jian-Guang], Han, Y.H.[Ya-Hong], Tang, J.H.[Jin-Hui], Hu, Q.H.[Qing-Hua], Jiang, J.M.[Jian-Min],
Semi-Supervised Image-to-Video Adaptation for Video Action Recognition,
Cyber(47), No. 4, April 2017, pp. 960-973.
IEEE DOI 1704
Cameras BibRef

Liu, J.J.[Jing-Jing], Chen, C.[Chao], Zhu, Y.[Yan], Liu, W.[Wei], Metaxas, D.N.[Dimitris N.],
Video Classification via Weakly Supervised Sequence Modeling,
CVIU(152), No. 1, 2016, pp. 79-87.
Elsevier DOI 1609
Video classification. for gesture and action classification. BibRef

Xu, Z.[Zhe], Wei, K.[Kun], Yang, E.[Erkun], Deng, C.[Cheng], Liu, W.[Wei],
Bilateral Relation Distillation for Weakly Supervised Temporal Action Localization,
PAMI(45), No. 10, October 2023, pp. 11458-11471.
IEEE DOI 2310
BibRef

Pei, L.S.[Li-Shen], Ye, M.[Mao], Zhao, X.Z.[Xue-Zhuan], Dou, Y.M.[Yu-Min], Bao, J.[Jiao],
Action recognition by learning temporal slowness invariant features,
VC(32), No. 11, November 2016, pp. 1395-1404.
WWW Link. 1611
BibRef

Tzelepis, C.[Christos], Galanopoulos, D.[Damianos], Mezaris, V.[Vasileios], Patras, I.[Ioannis],
Learning to detect video events from zero or very few video examples,
IVC(53), No. 1, 2016, pp. 35-44.
Elsevier DOI 1610
Video event detection BibRef

Gkalelis, N.[Nikolaos], Goulas, A.[Andreas], Galanopoulos, D.[Damianos], Mezaris, V.[Vasileios],
ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up Recognition and Explanation of Events in Video,
HVU21(3370-3378)
IEEE DOI 2109
Computational modeling, Detectors, Cognition, Object recognition BibRef

Zhou, Q.A.[Qi-Ang], Zhao, Q.[Qi],
Flexible Clustered Multi-Task Learning by Learning Representative Tasks,
PAMI(38), No. 2, February 2016, pp. 266-278.
IEEE DOI 1601
Covariance matrices BibRef

Zhou, Q.A.[Qi-Ang], Wang, G.[Gang], Jia, K.[Kui], Zhao, Q.[Qi],
Learning to Share Latent Tasks for Action Recognition,
ICCV13(2264-2271)
IEEE DOI 1403
Action Recognition; Latent Task BibRef

Yi, Y.[Yang], Lin, M.Q.[Mao-Qing],
Human action recognition with graph-based multiple-instance learning,
PR(53), No. 1, 2016, pp. 148-162.
Elsevier DOI 1602
Action recognition BibRef

Guo, Z.X.[Zi-Xin], Yi, Y.[Yang],
Graph-based multiple instance learning for action recognition,
ICIP13(3745-3749)
IEEE DOI 1402
Action Recognition BibRef

Sigari, M.H.[Mohamad-Hoseyn], Soltanian-Zadeh, H.[Hamid], Pourreza, H.R.[Hamid-Reza],
A framework for dynamic restructuring of semantic video analysis systems based on learning attention control,
IVC(53), No. 1, 2016, pp. 20-34.
Elsevier DOI 1610
Attention control BibRef

Yuan, Y.[Yuan], Qi, L.[Lei], Lu, X.Q.[Xiao-Qiang],
Action recognition by joint learning,
IVC(55, Part 2), No. 1, 2016, pp. 77-85.
Elsevier DOI 1612
Computer vision BibRef

Tang, J.[Jun], Jin, H.Q.[Hai-Qun], Tan, S.B.[Shou-Biao], Liang, D.[Dong],
Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization,
IVC(55, Part 2), No. 1, 2016, pp. 119-126.
Elsevier DOI 1612
Action recognition BibRef

Xu, T.T.[Tian-Tian], Zhu, F.[Fan], Wong, E.K.[Edward K.], Fang, Y.[Yi],
Dual many-to-one-encoder-based transfer learning for cross-dataset human action recognition,
IVC(55, Part 2), No. 1, 2016, pp. 127-137.
Elsevier DOI 1612
Cross-dataset BibRef

Richard, A.[Alexander], Gall, J.[Juergen],
A bag-of-words equivalent recurrent neural network for action recognition,
CVIU(156), No. 1, 2017, pp. 79-91.
Elsevier DOI 1702
BibRef
Earlier:
Temporal Action Detection Using a Statistical Language Model,
CVPR16(3131-3140)
IEEE DOI 1612
BibRef
Earlier:
A BoW-equivalent Recurrent Neural Network for Action Recognition,
BMVC15(xx-yy).
DOI Link 1601
Action recognition. untrimmed videos. BibRef

Kuehne, H.[Hilde], Richard, A.[Alexander], Gall, J.[Juergen],
Weakly supervised learning of actions from transcripts,
CVIU(163), No. 1, 2017, pp. 78-89.
Elsevier DOI 1712
Weak learning BibRef

Kuehne, H.[Hilde], Richard, A.[Alexander], Gall, J.[Juergen],
A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation,
PAMI(42), No. 4, April 2020, pp. 765-779.
IEEE DOI 2003
Videos, Hidden Markov models, Task analysis, Training, Supervised learning, Training data, Computational modeling, action recognition BibRef

Panareda Busto, P., Iqbal, A.[Ahsan], Gall, J.[Juergen],
Open Set Domain Adaptation for Image and Action Recognition,
PAMI(42), No. 2, February 2020, pp. 413-429.
IEEE DOI 2001
Videos, Feature extraction, Image recognition, Training, Task analysis, Face recognition, Protocols, Domain adaptation, action recognition BibRef

Iqbal, A.[Ahsan], Richard, A.[Alexander], Kuehne, H.[Hilde], Gall, J.[Juergen],
Recurrent Residual Learning for Action Recognition,
GCPR17(126-137).
Springer DOI 1711
BibRef
Earlier: A2, A3, A4, Only:
Weakly Supervised Action Learning with RNN Based Fine-to-Coarse Modeling,
CVPR17(1273-1282)
IEEE DOI 1711
Assistive technology, Data models, Hidden Markov models, Supervised learning, Training, Videos BibRef

Kim, M.Y.[Min-Young],
Dual soft assignment clustering algorithm for human action video clustering,
CVIU(155), No. 1, 2017, pp. 106-112.
Elsevier DOI 1702
Dual assignment clustering BibRef

Yang, Y.H.[Yan-Hua], Deng, C.[Cheng], Tao, D.P.[Da-Peng], Zhang, S.T.[Shao-Ting], Liu, W.[Wei], Gao, X.B.[Xin-Bo],
Latent Max-Margin Multitask Learning with Skelets for 3-D Action Recognition,
Cyber(47), No. 2, February 2017, pp. 439-448.
IEEE DOI 1702
cameras BibRef

Yang, Y., Deng, C., Gao, S., Liu, W., Tao, D., Gao, X.,
Discriminative Multi-instance Multitask Learning for 3D Action Recognition,
MultMed(19), No. 3, March 2017, pp. 519-529.
IEEE DOI 1702
Correlation BibRef

Gibson, J.[James], Katsamanis, A.[Athanasios], Romero, F.[Francisco], Xiao, B.[Bo], Georgiou, P.[Panayiotis], Narayanan, S.[Shrikanth],
Multiple Instance Learning for Behavioral Coding,
AffCom(8), No. 1, January 2017, pp. 81-94.
IEEE DOI 1703
Acoustics BibRef

Xu, K.[Ke], Jiang, X.H.[Xing-Hao], Sun, T.F.[Tan-Feng],
Two-Stream Dictionary Learning Architecture for Action Recognition,
CirSysVideo(27), No. 3, March 2017, pp. 567-576.
IEEE DOI 1703
Cameras BibRef

Xu, K.[Ke], Jiang, X.H.[Xing-Hao], Sun, T.F.[Tan-Feng],
Gait Recognition Based on Local Graphical Skeleton Descriptor With Pairwise Similarity Network,
MultMed(24), 2022, pp. 3265-3275.
IEEE DOI 2207
Skeleton, Feature extraction, Gait recognition, Dynamics, Legged locomotion, Deep learning, Gait recognition, skeleton, LGSD, pairwise similarity network BibRef

Ma, Z.G.[Zhi-Gang], Chang, X.J.[Xiao-Jun], Yang, Y.[Yi], Sebe, N.[Nicu], Hauptmann, A.G.[Alexander G.],
The Many Shades of Negativity,
MultMed(19), No. 7, July 2017, pp. 1558-1568.
IEEE DOI 1706
Training, negative examples. Animals, Detectors, Event detection, Feature extraction, Semantics, Support vector machines, Training, Attribute representation, attribute selection, complex event detection, selective, fine-grained, labeling BibRef

Chang, X.J.[Xiao-Jun], Yu, Y.L.[Yao-Liang], Yang, Y.[Yi], Xing, E.P.[Eric P.],
Semantic Pooling for Complex Event Analysis in Untrimmed Videos,
PAMI(39), No. 8, August 2017, pp. 1617-1632.
IEEE DOI 1707
BibRef
Earlier:
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers,
CVPR16(1884-1893)
IEEE DOI 1612
Algorithm design and analysis, Event detection, Feature extraction, Hidden Markov models, Semantics, Support vector machines, Videos, Complex event detection, event recognition, event recounting, nearly-isotonic SVM, semantic saliency. Fewer training samples for events. BibRef

Luvizon, D.C.[Diogo Carbonera], Tabia, H.[Hedi], Picard, D.[David],
Learning features combination for human action recognition from skeleton sequences,
PRL(99), No. 1, 2017, pp. 13-20.
Elsevier DOI 1710
Action recognition BibRef

Mokhtari, V.[Vahid], Lopes, L.S.[Luís Seabra], Pinho, A.J.[Armando J.],
Learning robot tasks with loops from experiences to enhance robot adaptability,
PRL(99), No. 1, 2017, pp. 57-66.
Elsevier DOI 1710
Robot task learning with loops BibRef

Zhang, H.B.[Hong-Bo], Lei, Q., Chen, D.S., Zhong, B.N., Peng, J., Du, J.X., Su, S.Z.[Song-Zhi],
Probability-based method for boosting human action recognition using scene context,
IET-CV(10), No. 6, 2016, pp. 528-536.
DOI Link 1609
Bayes methods BibRef

Zhang, H.B.[Hong-Bo], Li, S.Z.[Shao-Zi], Chen, S.Y.[Shu-Yuan], Su, S.Z.[Song-Zhi], Lin, X.M.[Xian-Ming], Cao, D.L.[Dong-Lin],
Locating and recognizing multiple human actions by searching for maximum score subsequences,
SIViP(9), No. 3, March 2015, pp. 705-714.
Springer DOI 1503
BibRef

Song, H., Wu, X., Yu, W., Jia, Y.,
Extracting Key Segments of Videos for Event Detection by Learning From Web Sources,
MultMed(20), No. 5, May 2018, pp. 1088-1100.
IEEE DOI 1805
Adaptation models, Event detection, Image segmentation, Semantics, Support vector machines, Training, Videos, Event detection, transfer learning BibRef

Wang, T.W.[Ting-Wei], Liu, C.C.[Chuan-Cai], Wang, L.T.[Lian-Tao], Ma, B.X.[Bing-Xian], Gu, X.J.[Xing-Jian],
Evolution modeling with multi-scale smoothing for action recognition,
JVCIR(55), 2018, pp. 778-788.
Elsevier DOI 1809
Action recognition, Multi-scale representation, Rank pooling, Evolution modeling, Dynamics BibRef

Zhang, L., Zhen, X., Shao, L., Song, J.,
Learning Match Kernels on Grassmann Manifolds for Action Recognition,
IP(28), No. 1, January 2019, pp. 205-215.
IEEE DOI 1810
Kernel, Manifolds, Feature extraction, Computational modeling, Image recognition, Semantics, neural networks BibRef

Zhang, H.[Hong], Xin, M.[Miao], Wang, S.H.[Shu-Hang], Yang, Y.F.[Yi-Fan], Zhang, L.[Lei], Wang, H.L.[He-Long],
End-to-End Temporal Attention Extraction and Human Action Recognition,
MVA(29), No. 7, October 2018, pp. 1127-1142.
Springer DOI 1810
BibRef

Chesneau, N.[Nicolas], Alahari, K.[Karteek], Schmid, C.[Cordelia],
Learning From Web Videos for Event Classification,
CirSysVideo(28), No. 10, October 2018, pp. 3019-3029.
IEEE DOI 1811
Use web videos for training. Videos, Training, Tires, Metadata, Visualization, YouTube, Feature extraction, Event classification, self-supervised learning BibRef

Rupprecht, C.[Christian], Kapil, A.[Ansh], Liu, N.[Nan], Ballan, L.[Lamberto], Tombari, F.[Federico],
Learning without prejudice: Avoiding bias in webly-supervised action recognition,
CVIU(173), 2018, pp. 24-32.
Elsevier DOI 1901
From data from the web. Action recognition, Webly-supervised learning BibRef

Huang, W., Fan, L., Harandi, M., Ma, L., Liu, H., Liu, W., Gan, C.,
Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks,
IP(28), No. 4, April 2019, pp. 1773-1782.
IEEE DOI 1901
backpropagation, feature extraction, image motion analysis, image recognition, image representation, object recognition, temporal pooling strategy BibRef

Zhang, J.X.[Jun-Xuan], Hu, H.F.[Hai-Feng],
Domain learning joint with semantic adaptation for human action recognition,
PR(90), 2019, pp. 196-209.
Elsevier DOI 1903
Knowledge adaptation, Two-stream network, Video representation, Action recognition, Cascaded convolution fusion strategy BibRef

Wang, Z.[Zheng], Chen, K.[Kai], Zhang, M.X.[Ming-Xing], He, P.L.[Pei-Lin], Wang, Y.J.[Ya-Jie], Zhu, P.[Ping], Yang, Y.[Yang],
Multi-Scale Aggregation Network for Temporal Action Proposals,
PRL(122), 2019, pp. 60-65.
Elsevier DOI 1904
BibRef

Liu, Y.[Yang], Lu, Z.Y.[Zhao-Yang], Li, J.[Jing], Yang, T.[Tao],
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition,
CirSysVideo(29), No. 8, August 2019, pp. 2416-2430.
IEEE DOI 1908
Videos, Dictionaries, Robustness, Machine learning, Sparse matrices, Noise reduction, Cameras, Action recognition, cross-view, distribution adaptation BibRef

Xu, B.H.[Bao-Han], Ye, H.[Hao], Zheng, Y.B.[Ying-Bin], Wang, H.[Heng], Luwang, T.Y.[Tian-Yu], Jiang, Y.G.[Yu-Gang],
Dense Dilated Network for Video Action Recognition,
IP(28), No. 10, October 2019, pp. 4941-4953.
IEEE DOI 1909
image recognition, image representation, neural nets, video signal processing, video surveillance, video analysis BibRef

Liu, J.Y.[Jia-Ying], Li, Y.H.[Yang-Hao], Song, S.J.[Si-Jie], Xing, J.L.[Jun-Liang], Lan, C.L.[Cui-Ling], Zeng, W.J.[Wen-Jun],
Multi-Modality Multi-Task Recurrent Neural Network for Online Action Detection,
CirSysVideo(29), No. 9, September 2019, pp. 2667-2682.
IEEE DOI 1909
Skeleton, Feature extraction, Recurrent neural networks, Hidden Markov models, Task analysis, Streaming media, Forecasting, joint classification-regression BibRef

Liu, H.J.[Hai-Jun], Wang, S.G.[Shi-Guang], Wang, W.[Wen], Cheng, J.[Jian],
Multi-Scale Based Context-Aware Net for Action Detection,
MultMed(22), No. 2, February 2020, pp. 337-348.
IEEE DOI 2001
Proposals, Feature extraction, Object detection, Logic gates, Message passing, Streaming media, gate function BibRef

Kong, Y.[Yu], Tao, Z.Q.[Zhi-Qiang], Fu, Y.[Yun],
Adversarial Action Prediction Networks,
PAMI(42), No. 3, March 2020, pp. 539-553.
IEEE DOI 2002
Videos, Feature extraction, Decoding, Accidents, Prediction methods, Training, Task analysis, Action prediction, action recognition, adversarial learning BibRef

Yang, Z.G.[Zhen-Guo], Li, Q.[Qing], Liu, W.Y.[Wen-Yin], Lv, J.M.[Jian-Ming],
Shared Multi-View Data Representation for Multi-Domain Event Detection,
PAMI(42), No. 5, May 2020, pp. 1243-1256.
IEEE DOI 2004
Data models, Dictionaries, Event detection, Social network services, Computational modeling, Task analysis, data representation learning BibRef

Yang, Z.G.[Zhen-Guo], Li, Q.[Qing], Xie, H.R.[Hao-Ran], Wang, Q.[Qi], Liu, W.Y.[Wen-Yin],
Learning representation from multiple media domains for enhanced event discovery,
PR(110), 2021, pp. 107640.
Elsevier DOI 2011
Data representation learning, Event detection, Social media, Multi-modality data BibRef

Hou, J., Wu, X., Wang, R., Luo, J., Jia, Y.,
Confidence-Guided Self Refinement for Action Prediction in Untrimmed Videos,
IP(29), 2020, pp. 6017-6031.
IEEE DOI 2005
Videos, Predictive models, Task analysis, Training, Hybrid power systems, Psychology, Machine learning, attention mechanism BibRef

Cai, J.H.[Jia-Hui], Hu, J.G.[Jian-Guo],
3D RANs: 3D Residual Attention Networks for action recognition,
VC(36), No. 6, June 2020, pp. 1261-1270.
WWW Link. 2005
BibRef

Spampinato, C.[Concetto], Palazzo, S.[Simone], d'Oro, P., Giordano, D., Shah, M.,
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos,
IJCV(128), No. 5, May 2020, pp. 1378-1397.
Springer DOI 2005
BibRef

Castagnolo, G.[Giulia], Spampinato, C.[Concetto], Rundo, F.[Francesco], Giordano, D.[Daniela], Palazzo, S.[Simone],
A Baseline on Continual Learning Methods for Video Action Recognition,
ICIP23(3240-3244)
IEEE DOI 2312
BibRef

Palazzo, S., Spampinato, C., Kavasidis, I., Giordano, D., Shah, M.,
Generative Adversarial Networks Conditioned by Brain Signals,
ICCV17(3430-3438)
IEEE DOI 1802
EEG while users look at images. brain, electroencephalography, image representation, learning (artificial intelligence), medical image processing, Visualization BibRef

Zheng, Y.D.[Yin-Dong], Liu, Z.Y.[Zhao-Yang], Lu, T.[Tong], Wang, L.M.[Li-Min],
Dynamic Sampling Networks for Efficient Action Recognition in Videos,
IP(29), 2020, pp. 7970-7983.
IEEE DOI 2007
Videos, Learning (artificial intelligence), Training, Testing, Machine learning, Image recognition, efficient action recognition BibRef

Jia, C., Ding, Z., Kong, Y., Fu, Y.,
Semi-Supervised Cross-Modality Action Recognition by Latent Tensor Transfer Learning,
CirSysVideo(30), No. 9, September 2020, pp. 2801-2814.
IEEE DOI 2009
Correlation, Training, Feature extraction, Target recognition, Tensors, Testing, Semantics, RGB-D action, cross-modality, low-rank tensor BibRef

Ng, Y.B., Fernando, B.,
Forecasting Future Action Sequences With Attention: A New Approach to Weakly Supervised Action Forecasting,
IP(29), 2020, pp. 8880-8891.
IEEE DOI 2009
Forecasting, Predictive models, Training, Uncertainty, Task analysis, Robots, Data models, Action forecasting, action sequence forecasting BibRef

Aliakbarian, M.S.[Mohammad Sadegh], Saleh, F.S.[Fatemeh Sadat], Salzmann, M.[Mathieu], Fernando, B.[Basura], Petersson, L.[Lars], Andersson, L.[Lars],
Encouraging LSTMs to Anticipate Actions Very Early,
ICCV17(280-289)
IEEE DOI 1802
Anticipate, don't need the whold sequence. feature extraction, image motion analysis, image recognition, image sequences, recurrent neural nets, Videos BibRef

Zhao, H.H.[Hao-Hua], Xue, W.C.[Wei-Chen], Li, X.B.[Xiao-Bo], Gu, Z.X.[Zhang-Xuan], Niu, L.[Li], Zhang, L.Q.[Li-Qing],
Multi-mode neural network for human action recognition,
IET-CV(14), No. 8, December 2020, pp. 587-596.
DOI Link 2012
BibRef

Wu, Q.Y.[Qian-Yu], Zhu, A.C.[Ai-Chun], Cui, R.[Ran], Wang, T.[Tian], Hu, F.Q.[Fang-Qiang], Bao, Y.P.[Ya-Ping], Snoussi, H.[Hichem],
Pose-Guided Inflated 3D ConvNet for action recognition in videos,
SP:IC(91), 2021, pp. 116098.
Elsevier DOI 2012
Action recognition, Pose estimation, Spatial-temporal information, Feature fusion BibRef

Zong, M.[Ming], Wang, R.[Ruili], Chen, X.[Xiubo], Chen, Z.[Zhe], Gong, Y.H.[Yuan-Hao],
Motion saliency based multi-stream multiplier ResNets for action recognition,
IVC(107), 2021, pp. 104108.
Elsevier DOI 2103
Action recognition, Motion saliency, Spatiotemporal interactive information, Multiplicative connections BibRef

Chen, Y.S.[Yao-Sen], Guo, B.[Bing], Shen, Y.[Yan], Wang, W.[Wei], Lu, W.C.[Wei-Chen], Suo, X.H.[Xin-Hua],
Boundary graph convolutional network for temporal action detection,
IVC(109), 2021, pp. 104144.
Elsevier DOI 2105
Temporal action detection, Graph convolutional network, Temproal action proposals, Video features BibRef

Paoletti, G.[Giancarlo], Cavazza, J.[Jacopo], Beyan, C.[Cigdem], del Bue, A.[Alessio],
Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning,
ICPR21(6035-6042)
IEEE DOI 2105
Protocols, Laplace equations, Data analysis, Clustering methods, Pipelines, Supervised learning, Machine learning BibRef

Su, H.S.[Hai-Sheng], Zhao, X.[Xu], Lin, T.W.[Tian-Wei], Liu, S.M.[Shu-Ming], Hu, Z.L.[Zhi-Lan],
Transferable Knowledge-Based Multi-Granularity Fusion Network for Weakly Supervised Temporal Action Detection,
MultMed(23), 2021, pp. 1503-1515.
IEEE DOI 2106
Feature extraction, Task analysis, Training, Object detection, Kernel, Convolution, Visualization, Boundary regression, weak supervision BibRef

Lai, K.[Kenneth], Yanushkevich, S.N.[Svetlana N.], Shmerko, V.[Vlad], Hou, M.[Ming],
Capturing causality and bias in human action recognition,
PRL(147), 2021, pp. 164-171.
Elsevier DOI 2106
Machine learning, Decision support, Human action recognition, Machine reasoning, Belief networks BibRef

Kim, J.H.[Jong-Hyun], Li, G.[Gen], Yun, I.[Inyong], Jung, C.[Cheolkon], Kim, J.[Joongkyu],
Weakly-supervised temporal attention 3D network for human action recognition,
PR(119), 2021, pp. 108068.
Elsevier DOI 2106
Action recognition, Temporal attention, Convolutional neural network, Weakly-supervised learning, Video classification BibRef

Yang, W.F.[Wen-Fei], Zhang, T.Z.[Tian-Zhu], Mao, Z.D.[Zhen-Dong], Zhang, Y.D.[Yong-Dong], Tian, Q.[Qi], Wu, F.[Feng],
Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection,
IP(30), 2021, pp. 5848-5861.
IEEE DOI 2106
Proposals, Feature extraction, Image segmentation, Scalability, Noise measurement, Graph neural networks, GSM, Weakly supervised, structure-aware BibRef

Wu, J.M.[Jia-Min], Zhang, T.Z.[Tian-Zhu], Zhang, Z.[Zhe], Wu, F.[Feng], Zhang, Y.D.[Yong-Dong],
Motion-modulated Temporal Fragment Alignment Network For Few-Shot Action Recognition,
CVPR22(9141-9150)
IEEE DOI 2210
Image recognition, Motion segmentation, Modulation, Benchmark testing, Task analysis, Action and event recognition BibRef

Yang, W.F.[Wen-Fei], Zhang, T.Z.[Tian-Zhu], Yu, X.Y.[Xiao-Yuan], Qi, T.[Tian], Zhang, Y.D.[Yong-Dong], Wu, F.[Feng],
Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection,
CVPR21(53-63)
IEEE DOI 2111
Training, Uncertainty, Collaboration, Benchmark testing, Reliability engineering BibRef

Leng, C.J.[Chuan-Jiang], Ding, Q.C.[Qi-Chuan], Wu, C.D.[Cheng-Dong], Chen, A.[Ange],
Augmented two stream network for robust action recognition adaptive to various action videos,
JVCIR(81), 2021, pp. 103344.
Elsevier DOI 2112
Two-stream network, Action recognition, Data skew BibRef

Zhang, C.Y.[Chun-Yang], Xiao, Y.Y.[Yong-Yi], Lin, J.C.[Jin-Cheng], Chen, C.L.P.[C. L. Philip], Liu, W.X.[Wen-Xi], Tong, Y.H.[Yu-Hong],
3-D Deconvolutional Networks for the Unsupervised Representation Learning of Human Motions,
Cyber(52), No. 1, January 2022, pp. 398-410.
IEEE DOI 2201
Machine learning, Task analysis, Correlation, Optimization, Feature extraction, Convolution, Data models, video representation learning BibRef

Liu, Y.[Yuan], Chen, J.Y.[Jing-Yuan], Chen, X.P.[Xin-Peng], Deng, B.[Bing], Huang, J.Q.[Jian-Qiang], Hua, X.S.[Xian-Sheng],
Centerness-Aware Network for Temporal Action Proposal,
CirSysVideo(32), No. 1, January 2022, pp. 5-16.
IEEE DOI 2201
Proposals, Feature extraction, Detectors, Task analysis, Visualization, Video sequences, Object detection, multi-scale center detector BibRef

Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Video Moment Retrieval Without a Single Annotated Video,
CirSysVideo(32), No. 3, March 2022, pp. 1646-1657.
IEEE DOI 2203
Visualization, Task analysis, Generators, Training, Graph neural networks, Semantics, Detectors, unpaired learning BibRef

Sun, B.[Bin], Kong, D.[Dehui], Wang, S.[Shaofan], Li, J.H.[Jing-Hua], Yin, B.C.[Bao-Cai], Luo, X.N.[Xiao-Nan],
GAN for vision, KG for relation: A two-stage network for zero-shot action recognition,
PR(126), 2022, pp. 108563.
Elsevier DOI 2204
Action recognition, Zero-shot learning, Generative adversarial networks, Graph convolution network BibRef

Luo, H.[Haonan], Lin, G.S.[Guo-Sheng], Yao, Y.Z.[Ya-Zhou], Tang, Z.M.[Zhen-Min], Wu, Q.Y.[Qing-Yao], Hua, X.S.[Xian-Sheng],
Dense Semantics-Assisted Networks for Video Action Recognition,
CirSysVideo(32), No. 5, May 2022, pp. 3073-3084.
IEEE DOI 2205
Semantics, Image segmentation, Streaming media, Image recognition, Training, Target recognition, feature fusion BibRef

He, C.[Chen], Zhang, J.[Jing], Yao, J.C.[Jia-Cheng], Zhuo, L.[Li], Tian, Q.[Qi],
Meta-Learning Paradigm and CosAttn for Streamer Action Recognition in Live Video,
SPLetters(29), 2022, pp. 1097-1101.
IEEE DOI 2205
Streaming media, Training, Feature extraction, Testing, Prototypes, Task analysis, Optimization, Live video, CosAttn BibRef

Wang, W.N.[Wei-Ning], Lin, T.W.[Tian-Wei], He, D.L.[Dong-Liang], Li, F.[Fu], Wen, S.L.[Shi-Lei], Wang, L.[Liang], Liu, J.[Jing],
Semi-Supervised Temporal Action Proposal Generation via Exploiting 2-D Proposal Map,
MultMed(24), 2022, pp. 3624-3635.
IEEE DOI 2207
Proposals, Data models, Task analysis, Semisupervised learning, Training, Supervised learning, Predictive models, pseudo label BibRef

Tang, Y.P.[Ye-Peng], Wang, W.N.[Wei-Ning], Zhang, C.J.[Chun-Jie], Liu, J.[Jing], Zhao, Y.[Yao],
Temporal Action Proposal Generation With Action Frequency Adaptive Network,
MultMed(26), 2024, pp. 2340-2353.
IEEE DOI 2402
Proposals, Task analysis, Data models, Time-frequency analysis, Representation learning, Predictive models, Information science, action frequency BibRef

Li, B.R.[Bai-Rong], Liu, R.X.[Rui-Xin], Chen, T.Q.[Tian-Quan], Zhu, Y.S.[Yue-Sheng],
Weakly Supervised Temporal Action Detection With Temporal Dependency Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4473-4485.
IEEE DOI 2207
Videos, Proposals, Transformers, Task analysis, Annotations, Training, Semantics, Weakly supervised learning, temporal action detection BibRef

Li, B.R.[Bai-Rong], Guo, B.[Biao], Zhu, Y.S.[Yue-Sheng], Yin, J.F.[Jian-Feng], Ji, X.L.[Xiang-Li],
Superframe-Based Temporal Proposals for Weakly Supervised Temporal Action Detection,
MultMed(25), 2023, pp. 3628-3641.
IEEE DOI 2310
BibRef

Kumawat, S.[Sudhakar], Verma, M.[Manisha], Nakashima, Y.[Yuta], Raman, S.[Shanmuganathan],
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition,
PAMI(44), No. 9, September 2022, pp. 4839-4851.
IEEE DOI 2208
Convolution, Kernel, Correlation, Complexity theory, Standards, Short-term fourier transform, human action recognition BibRef

Chen, B.[Bo], Tang, H.Y.[Hong-Ying], Zhang, Z.B.[Ze-Bin], Tong, G.J.[Guan-Jun], Li, B.Q.[Bao-Qing],
Video-based action recognition using spurious-3D residual attention networks,
IET-IPR(16), No. 11, 2022, pp. 3097-3111.
DOI Link 2208
BibRef

Fu, J.[Jie], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Semantic-Aware Spatial-Temporal Attention for Interpretable Action Recognition,
CirSysVideo(32), No. 8, August 2022, pp. 5213-5224.
IEEE DOI 2208
Visualization, Semantics, Task analysis, Feature extraction, Solid modeling, Predictive models, Semantic-aware, action recognition BibRef

Wei, D.F.[Da-Feng], Tian, Y.[Ye], Wei, L.Q.[Li-Qing], Zhong, H.[Hong], Chen, S.Q.[Si-Qian], Pu, S.L.[Shi-Liang], Lu, H.T.[Hong-Tao],
Efficient dual attention SlowFast networks for video action recognition,
CVIU(222), 2022, pp. 103484.
Elsevier DOI 2209
Learn temporal and spatial together. Efficient video action recognition, Dual attention mechanism, Efficient SlowFast networks BibRef

Nie, L.S.[Lan-Shun], Li, X.[Xue], Gong, T.Y.[Tian-Ying], Zhan, D.[Dechen],
Few shot learning-based fast adaptation for human activity recognition,
PRL(159), 2022, pp. 100-107.
Elsevier DOI 2206
BibRef

Monfort, M.[Mathew], Pan, B.[Bowen], Ramakrishnan, K.[Kandan], Andonian, A.[Alex], McNamara, B.A.[Barry A.], Lascelles, A.[Alex], Fan, Q.F.[Quan-Fu], Gutfreund, D.[Dan], Feris, R.S.[Rogerio Schmidt], Oliva, A.[Aude],
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding,
PAMI(44), No. 12, December 2022, pp. 9434-9445.
IEEE DOI 2212
Visualization, Annotations, Training, Analytical models, Semantics, Convolutional neural networks, machine learning, video, neural nets BibRef

Meng, Y.[Yue], Lin, C.C.[Chung-Ching], Panda, R.[Rameswar], Sattigeri, P.[Prasanna], Karlinsky, L.[Leonid], Oliva, A.[Aude], Saenko, K.[Kate], Feris, R.S.[Rogerio S.],
Ar-net: Adaptive Frame Resolution for Efficient Action Recognition,
ECCV20(VII:86-104).
Springer DOI 2011
BibRef

Tang, Y.P.[Yi-Ping], Zheng, Y.[Yang], Wei, C.[Chen], Guo, K.T.[Kai-Tai], Hu, H.H.[Hai-Hong], Liang, J.[Jimin],
Video representation learning for temporal action detection using global-local attention,
PR(134), 2023, pp. 109135.
Elsevier DOI 2212
Temporal action detection, Video representation, Untrimmed video analysis BibRef

Gao, Z.[Zan], Zhao, Y.[Yibo], Zhang, H.[Hua], Chen, D.[Da], Liu, A.A.[An-An], Chen, S.Y.[Sheng-Yong],
A Novel Multiple-View Adversarial Learning Network for Unsupervised Domain Adaptation Action Recognition,
Cyber(52), No. 12, December 2022, pp. 13197-13211.
IEEE DOI 2212
Feature extraction, Convolution, Training, Adversarial machine learning, Target recognition, Robustness, unsupervised domain adaptation action recognition BibRef

Omi, K.[Kazuki], Kimata, J.[Jun], Tamaki, T.[Toru],
Model-Agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition,
IEICE(E105-D), No. 12, December 2022, pp. 2119-2126.
WWW Link. 2212
BibRef

Yang, Y.[Yang], Liu, G.J.[Guang-Jun], Gao, X.H.[Xue-Hao],
Motion Guided Attention Learning for Self-Supervised 3D Human Action Recognition,
CirSysVideo(32), No. 12, December 2022, pp. 8623-8634.
IEEE DOI 2212
Task analysis, Semantics, Generators, Costs, Representation learning, Recurrent neural networks, Self-supervised learning, motion attention BibRef

Konforti, Y.[Yael], Shpigler, A.[Alon], Lerner, B.[Boaz], Bar-Hillel, A.[Aharon],
SIGN: Statistical Inference Graphs Based on Probabilistic Network Activity Interpretation,
PAMI(45), No. 3, March 2023, pp. 3783-3797.
IEEE DOI 2302
Visualization, Hidden Markov models, Training, Neural networks, Probabilistic logic, Convolutional neural networks, statistical inference BibRef

Yu, B.X.B.[Bruce X.B.], Liu, Y.[Yan], Zhang, X.[Xiang], Zhong, S.H.[Sheng-Hua], Chan, K.C.C.[Keith C.C.],
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos,
PAMI(45), No. 3, March 2023, pp. 3522-3538.
IEEE DOI 2302
Skeleton, Videos, Data models, Computational modeling, Hidden Markov models, Writing, Solid modeling, ensemble learning BibRef

Tong, A.[Anyang], Tang, C.[Chao], Wang, W.J.[Wen-Jian],
Semi-Supervised Action Recognition From Temporal Augmentation Using Curriculum Learning,
CirSysVideo(33), No. 3, March 2023, pp. 1305-1319.
IEEE DOI 2303
Training, Labeling, Data models, Feature extraction, Noise measurement, Image recognition, Image classification, temporal augmentation BibRef

Nitta, T.[Tomoya], Hirakawa, T.[Tsubasa], Fujiyoshi, H.[Hironobu], Tamaki, T.[Toru],
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition,
IEICE(E106-D), No. 3, March 2023, pp. 391-400.
WWW Link. 2303
Attention Branch Network. Instance segmentation BibRef

Korban, M.[Matthew], Li, X.[Xin],
Semantics-enhanced early action detection using dynamic dilated convolution,
PR(140), 2023, pp. 109595.
Elsevier DOI 2305
Early action detection, Action semantics, Dilated convolutional network BibRef

Wu, J.L.[Jian-Long], Sun, W.[Wei], Gan, T.[Tian], Ding, N.[Ning], Jiang, F.[Feijun], Shen, J.[Jialie], Nie, L.Q.[Li-Qiang],
Neighbor-Guided Consistent and Contrastive Learning for Semi-Supervised Action Recognition,
IP(32), 2023, pp. 2215-2227.
IEEE DOI 2305
Training, Task analysis, Semisupervised learning, Convolution, Computational modeling, contrastive learning BibRef

Zhong, Z.K.[Zhuo-Kun], Hou, Z.J.[Zhen-Jie], Liang, J.Z.[Jiu-Zhen], Lin, E.[En], Shi, H.Y.[Hai-Yong],
Multimodal cooperative self-attention network for action recognition,
IET-IPR(17), No. 6, 2023, pp. 1775-1783.
DOI Link 2305
image fusion BibRef

Yang, M.[Min], Chen, G.[Guo], Zheng, Y.D.[Yin-Dong], Lu, T.[Tong], Wang, L.M.[Li-Min],
BasicTAD: An astounding RGB-Only baseline for temporal action detection,
CVIU(232), 2023, pp. 103692.
Elsevier DOI 2305
BibRef

Wang, W.Q.[Wen-Qian], Chang, F.L.[Fa-Liang], Zhang, J.H.[Jun-Hao], Yan, R.[Rui], Liu, C.S.[Chun-Sheng], Wang, B.[Bin], Shou, M.Z.[Mike Zheng],
Magi-Net: Meta Negative Network for Early Activity Prediction,
IP(32), 2023, pp. 3254-3265.
IEEE DOI 2306
Predictive models, Video sequences, Task analysis, Optimization, Prediction algorithms, Adaptation models, Training, meta learning BibRef

Hu, Y.F.[Yu-Fan], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Scene-Aware Spatio-Temporal GNNs for Few-Shot Early Action Prediction,
MultMed(25), 2023, pp. 2061-2073.
IEEE DOI 2306
Videos, Task analysis, Graph neural networks, Feature extraction, Predictive models, Visualization, Convolution, Few-shot learning, graph neural network BibRef

Li, S.[Shibao], Wang, Z.Y.[Zhao-Yu], Liu, Y.X.[Yi-Xuan], Zhang, Y.[Yunwu], Zhu, J.Z.[Jin-Ze], Cui, X.R.[Xue-Rong], Liu, J.H.[Jian-Hang],
FSformer: Fast-Slow Transformer for video action recognition,
IVC(137), 2023, pp. 104740.
Elsevier DOI 2309
Action recognition, Two-stream, Transformer, Self-attention BibRef

Wang, H.F.[Hua-Feng], Li, H.L.[Han-Lin], Liu, W.Q.[Wan-Quan], Gu, X.F.[Xian-Feng],
Temporal information oriented motion accumulation and selection network for RGB-based action recognition,
IVC(137), 2023, pp. 104785.
Elsevier DOI 2309
Action recognition, Motion accumulation, Selective network, Motion BibRef

Quan, Z.Z.[Zhen-Zhen], Chen, Q.S.[Qing-Shan], Li, Y.J.[Yu-Jun], Liu, Z.[Zhi], Cui, Y.[Yan],
ARCTIC: A knowledge distillation approach via attention-based relation matching and activation region constraint for RGB-to-Infrared videos action recognition,
CVIU(237), 2023, pp. 103853.
Elsevier DOI 2311
Action recognition, Attention, Knowledge distillation, Infrared BibRef

Zhou, J.Q.[Jia-Qi], Fu, Z.[Zehua], Huang, Q.Y.[Qiu-Yu], Liu, Q.J.[Qing-Jie], Wang, Y.H.[Yun-Hong],
LgNet: A Local-Global Network for Action Recognition and Beyond,
MultMed(25), 2023, pp. 5192-5205.
IEEE DOI 2311
BibRef

Assefa, M.[Maregu], Jiang, W.[Wei], Gedamu, K.[Kumie], Yilma, G.[Getinet], Kumeda, B.[Bulbula], Ayalew, M.[Melese],
Self-Supervised Scene-Debiasing for Video Representation Learning via Background Patching,
MultMed(25), 2023, pp. 5500-5515.
IEEE DOI 2311
Supress background influence in action learning. BibRef

Bousmina, A.[Abir], Selmi, M.[Mouna], Ben Rhaiem, M.A.[Mohamed Amine], Farah, I.R.[Imed Riadh],
A Hybrid Approach Based on GAN and CNN-LSTM for Aerial Activity Recognition,
RS(15), No. 14, 2023, pp. 3626.
DOI Link 2307
From overhead views, UAV. BibRef

Assefa, M.[Maregu], Jiang, W.[Wei], Zhan, J.[Jinyu], Gedamu, K.[Kumie], Yilma, G.[Getinet], Ayalew, M.[Melese], Adhikari, D.[Deepak],
Audio-Visual Contrastive and Consistency Learning for Semi-Supervised Action Recognition,
MultMed(26), 2024, pp. 3491-3504.
IEEE DOI 2402
Task analysis, Predictive models, Visualization, Semisupervised learning, Correlation, Reliability, Optical flow. BibRef

Wang, X.[Xiang], Zhang, S.W.[Shi-Wei], Qing, Z.W.[Zhi-Wu], Zuo, Z.R.[Zheng-Rong], Gao, C.X.[Chang-Xin], Jin, R.[Rong], Sang, N.[Nong],
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-Shot Action Recognition,
PR(147), 2024, pp. 110110.
Elsevier DOI Code:
WWW Link. 2312
Few-shot action recognition, Set matching, Semi-supervised few-shot action recognition, Unsupervised few-shot action recognition BibRef

Wang, X.[Xiang], Zhang, S.W.[Shi-Wei], Qing, Z.W.[Zhi-Wu], Tang, M.Q.[Ming-Qian], Zuo, Z.R.[Zheng-Rong], Gao, C.X.[Chang-Xin], Jin, R.[Rong], Sang, N.[Nong],
Hybrid Relation Guided Set Matching for Few-shot Action Recognition,
CVPR22(19916-19925)
IEEE DOI 2210
Measurement, Training, Semantics, Benchmark testing, Kinetic theory, Task analysis, Self- semi- meta- unsupervised learning BibRef

Wang, X.[Xiang], Zhang, S.W.[Shi-Wei], Cen, J.[Jun], Gao, C.X.[Chang-Xin], Zhang, Y.Y.[Ying-Ya], Zhao, D.L.[De-Li], Sang, N.[Nong],
CLIP-guided Prototype Modulating for Few-shot Action Recognition,
IJCV(132), No. 6, June 2024, pp. 1899-1912.
Springer DOI 2406
BibRef

Su, T.[Taiyi], Wang, H.[Hanli], Qi, Q.P.[Qiu-Ping], Wang, L.[Lei], He, B.[Bin],
Transductive Learning With Prior Knowledge for Generalized Zero-Shot Action Recognition,
CirSysVideo(34), No. 1, January 2024, pp. 260-273.
IEEE DOI Code:
WWW Link. 2401
BibRef

Zheng, Z.W.[Zi-Wei], Yang, L.[Le], Wang, Y.L.[Yu-Lin], Zhang, M.[Miao], He, L.J.[Li-Jun], Huang, G.[Gao], Li, F.[Fan],
Dynamic Spatial Focus for Efficient Compressed Video Action Recognition,
CirSysVideo(34), No. 2, February 2024, pp. 695-708.
IEEE DOI 2402
Image coding, Task analysis, Optical flow, Decoding, Feature extraction, Dynamics, Video action recognition, dynamic neural networks BibRef

Wang, Y.L.[Yu-Lin], Yue, Y.[Yang], Lin, Y.Z.[Yuan-Ze], Jiang, H.J.[Hao-Jun], Lai, Z.H.[Zi-Hang], Kulikov, V.[Victor], Orlov, N.[Nikita], Shi, H.[Humphrey], Huang, G.[Gao],
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition,
CVPR22(20030-20040)
IEEE DOI 2210
Training, Adaptation models, Computational modeling, Redundancy, Pipelines, Reinforcement learning, Benchmark testing, Efficient learning and inferences BibRef

Wang, Y.L.[Yu-Lin], Yue, Y.[Yang], Xu, X.H.[Xin-Hong], Hassani, A.[Ali], Kulikov, V.[Victor], Orlov, N.[Nikita], Song, S.[Shiji], Shi, H.[Humphrey], Huang, G.[Gao],
AdaFocusV3: On Unified Spatial-Temporal Dynamic Video Recognition,
ECCV22(IV:226-243).
Springer DOI 2211
BibRef

Gao, Y.[Yue], Lu, J.X.[Jia-Xuan], Li, S.Q.[Si-Qi], Ma, N.[Nan], Du, S.Y.[Shao-Yi], Li, Y.P.[Yi-Peng], Dai, Q.H.[Qiong-Hai],
Action Recognition and Benchmark Using Event Cameras,
PAMI(45), No. 12, December 2023, pp. 14081-14097.
IEEE DOI 2311
BibRef

Gao, Y.[Yue], Lu, J.X.[Jia-Xuan], Li, S.Q.[Si-Qi], Li, Y.P.[Yi-Peng], Du, S.[Shaoyi],
Hypergraph-Based Multi-View Action Recognition Using Event Cameras,
PAMI(46), No. 10, October 2024, pp. 6610-6622.
IEEE DOI 2409
Cameras, Feature extraction, Neural networks, Vision sensors, Task analysis, Semantics, Robot vision systems, hypergraph neural network BibRef

Ma, N.[Nan], Wu, Z.X.[Zhi-Xuan], Feng, Y.F.[Yi-Fan], Wang, C.[Cheng], Gao, Y.[Yue],
Multi-View Time-Series Hypergraph Neural Network for Action Recognition,
IP(33), 2024, pp. 3301-3313.
IEEE DOI 2405
Neural networks, Feature extraction, Correlation, Adaptation models, Training, Heuristic algorithms, Data models, multi-view time-series hypergraph neural network BibRef

Di, J.[Jirui], Hu, Z.P.[Zheng-Ping], Bi, S.[Shuai], Zhang, H.[Hehao], Wang, Y.[Yulu], Sun, Z.[Zhe],
Temporal refinement network: Combining dynamic convolution and multi-scale information for fine-grained action recognition,
IVC(147), 2024, pp. 105058.
Elsevier DOI 2406
Fine-grained action recognition, Temporal refinement block, Temporal pyramidal network BibRef

Zhu, H.[Hao], Xie, S.W.[Shao-Wen], Liu, Z.[Zhen], Liu, F.Y.[Feng-Yi], Zhang, Q.[Qi], Zhou, Y.[You], Lin, Y.[Yi], Ma, Z.[Zhan], Cao, X.[Xun],
Disorder-Invariant Implicit Neural Representation,
PAMI(46), No. 8, August 2024, pp. 5463-5478.
IEEE DOI 2407
BibRef
Earlier: A2, A1, A3, A5, A6, A9, A8, Only:
DINER: Disorder-Invariant Implicit Neural Representation,
CVPR23(6143-6152)
IEEE DOI 2309
Encoding, Task analysis, Optimization, Inverse problems, Frequency modulation, Training, Disorder-invariance, hash-table, inverse problem optimization BibRef

Zou, M.H.[Ming-Hao], Zeng, Q.T.[Qing-Tian], Zhang, X.[Xue],
Weakly-Supervised Action Learning in Procedural Task Videos via Process Knowledge Decomposition,
CirSysVideo(34), No. 7, July 2024, pp. 5575-5588.
IEEE DOI 2407
Task analysis, Annotations, Training, Predictive models, Costs, Context modeling, Prediction algorithms, local attention BibRef

Liu, Z.S.[Zhi-Song], Courant, R.[Robin], Kalogeiton, V.[Vicky],
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild,
IJCV(132), No. 8, August 2024, pp. 2885-2906.
Springer DOI 2408
BibRef

Zhang, X.M.[Xue-Mei], Zhao, P.[Peng], Ji, J.S.[Jin-Sheng], Lu, X.[Xiankai], Yin, Y.L.[Yi-Long],
Video Corpus Moment Retrieval via Deformable Multigranularity Feature Fusion and Adversarial Training,
CirSysVideo(34), No. 8, August 2024, pp. 6686-6698.
IEEE DOI 2408
Training, Task analysis, Location awareness, Feature extraction, Semantics, Visualization, Transformers, adversarial training BibRef

Alfasly, S.[Saghir], Lu, J.[Jian], Xu, C.[Chen], Li, Y.[Yu], Zou, Y.[Yuru],
Auxiliary audio-textual modalities for better action recognition on vision-specific annotated videos,
PR(156), 2024, pp. 110808.
Elsevier DOI 2408
BibRef
Earlier: A1, A2, A3, A5, Only:
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos,
CVPR22(20176-20185)
IEEE DOI 2210
Action recognition, Multimodal training, Large language models, Video transformer, Audio-visual training. Training, Bridges, Visualization, Fuses, Computational modeling, Semantics, Transfer learning, Action and event recognition, Vision applications and systems BibRef

Wang, R.M.[Ruo-Mei], Feng, J.W.[Jia-Wei], Zhang, F.[Fuwei], Luo, X.N.[Xiao-Nan], Luo, Y.M.[Yuan-Mao],
Modality-Aware Heterogeneous Graph for Joint Video Moment Retrieval and Highlight Detection,
CirSysVideo(34), No. 9, September 2024, pp. 8896-8911.
IEEE DOI 2410
Task analysis, Correlation, Feature extraction, Cognition, Decoding, Generators, Video moment retrieval, cross-modal interaction BibRef

Zhu, A.[Anlei], Wang, Y.H.[Ying-Hui], Yang, J.L.[Jin-Long], Yan, T.[Tao], Ma, H.[Haomiao], Li, W.[Wei],
YOWOv3: A Lightweight Spatio-Temporal Joint Network for Video Action Detection,
CirSysVideo(34), No. 9, September 2024, pp. 8148-8160.
IEEE DOI 2410
Feature extraction, Convolution, Computational modeling, Solid modeling, Load modeling, Real-time systems, edge devices BibRef

Bicsi, L.[Lucian], Alexe, B.[Bogdan], Ionescu, R.T.[Radu Tudor], Leordeanu, M.[Marius],
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition,
LIMIT23(953-962)
IEEE DOI 2401
BibRef

Ming, Y.[Yue], Xiong, L.[Lu], Jia, X.[Xia], Zheng, Q.F.[Qing-Fang], Zhou, J.W.[Jiang-Wan], Feng, F.[Fan], Hu, N.N.[Nan-Nan],
Frequency Enhancement Network for Efficient Compressed Video Action Recognition,
ICIP23(825-829)
IEEE DOI 2312
BibRef

Hasan, Z.[Zahid], Ahmed, M.[Masud], Faridee, A.Z.M.[Abu Zaher Md], Purushotham, S.[Sanjay], Kwon, H.S.[Hee-Sung], Lee, H.[Hyungtae], Roy, N.[Nirmalya],
NEV-NCD: Negative Learning, Entropy, and Variance Regularization Based Novel Action Categories Discovery,
ICIP23(2720-2724)
IEEE DOI 2312
BibRef

Zhang, J.H.[Jiang-Hao], Zhong, X.[Xian], Liu, W.X.[Wen-Xuan], Jiang, K.[Kui], Yang, Z.W.[Zheng-Wei], Wang, Z.[Zheng],
Implicit Attention-Based Cross-Modal Collaborative Learning for Action Recognition,
ICIP23(3020-3024)
IEEE DOI 2312
BibRef

Chang, S.[Shuning], Wang, P.[Pichao], Wang, F.[Fan], Feng, J.S.[Jia-Shi], Shou, M.Z.[Mike Zheng],
DOAD: Decoupled One Stage Action Detection Network,
HVU23(3123-3232)
IEEE DOI 2309
BibRef

Liu, S.M.[Shu-Ming], Xu, M.M.[Meng-Meng], Zhao, C.[Chen], Zhao, X.[Xu], Ghanem, B.[Bernard],
ETAD: Training Action Detection End to End on a Laptop,
ECV23(4525-4534)
IEEE DOI 2309
BibRef

He, B.[Bo], Yang, X.T.[Xi-Tong], Wang, H.Y.[Han-Yu], Wu, Z.X.[Zu-Xuan], Chen, H.[Hao], Huang, S.[Shuaiyi], Ren, Y.X.[Yi-Xuan], Lim, S.N.[Ser-Nam], Shrivastava, A.[Abhinav],
Towards Scalable Neural Representation for Diverse Videos,
CVPR23(6132-6142)
IEEE DOI 2309
BibRef

Zhang, C.H.[Chu-Han], Gupta, A.[Ankush], Zisserman, A.[Andrew],
Is an Object-centric Video Representation Beneficial for Transfer?,
ACCV22(IV:379-397).
Springer DOI 2307
BibRef

Ye, N.[Na], Zhang, X.[Xing], Yan, D.W.[Da-Wei], Dong, W.[Wei], Yan, Q.S.[Qing-Sen],
SCOAD: Single-Frame Click Supervision for Online Action Detection,
ACCV22(IV:223-238).
Springer DOI 2307
BibRef

Han, H.F.[Hong-Feng], Lu, Z.W.[Zhi-Wu], Wen, J.R.[Ji-Rong],
Binary Neural Network for Video Action Recognition,
MMMod23(I: 95-106).
Springer DOI 2304
BibRef

Guo, J.X.[Jin-Xin], Zhang, J.Q.[Jia-Qiang], Zhang, X.J.[Xiao-Jing], Ma, M.[Ming],
LAE-Net: Light and Efficient Network for Compressed Video Action Recognition,
MMMod23(II: 265-276).
Springer DOI 2304
BibRef

Shah, K.[Ketul], Shah, A.[Anshul], Lau, C.P.[Chun Pong], de Melo, C.M.[Celso M.], Chellapp, R.[Rama],
Multi-View Action Recognition using Contrastive Learning,
WACV23(3370-3380)
IEEE DOI 2302
Training, Synchronization, Standards, Videos, Synthetic data, Algorithms: Video recognition and understanding (tracking BibRef

Lu, Y.F.[Yi-Fan], Singh, G.[Gurkirt], Saha, S.[Suman], Van Gool, L.J.[Luc J.],
Exploiting Instance-based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-adaptive Action Detection,
WACV23(4134-4145)
IEEE DOI 2302
Training, Protocols, Semantic segmentation, Source coding, Self-supervised learning BibRef

Conte, D.[Donatello], Fioretti, G.G.[Giuliano Giovanni], Sansone, C.[Carlo],
On the Importance of Temporal Features in Domain Adaptation Methods for Action Recognition,
SSSPR22(264-273).
Springer DOI 2301
BibRef

Gebotys, B.[Brennan], Wong, A.[Alexander], Clausi, D.A.[David A.],
M2A: Motion Aware Attention for Accurate Video Action Recognition,
CRV22(83-89)
IEEE DOI 2301
Codes, Computational modeling, Neural networks, Machine learning, Benchmark testing, video action recognition, motion, attention BibRef

Tai, T.M.[Tsung-Ming], Fiameni, G.[Giuseppe], Lee, C.K.[Cheng-Kuang], See, S.[Simon], Lanz, O.[Oswald],
Unified Recurrence Modeling for Video Action Anticipation,
ICPR22(3273-3279)
IEEE DOI 2212
Annotations, Message passing, Computational modeling, Decision making, Predictive models BibRef

Han, Y.F.[Yun-Fei], Tan, S.[Shan],
TwinLSTM: Two-channel LSTM Network for Online Action Detection,
ICPR22(3310-3317)
IEEE DOI 2212
Recurrent neural networks, Fuses, Feature extraction, History, Long short term memory BibRef

Vu, D.Q.[Duc-Quang], Le, N.T.H.[Ngan T.H.], Wang, J.C.[Jia-Ching],
(2+1)D Distilled ShuffleNet: A Lightweight Unsupervised Distillation Network for Human Action Recognition,
ICPR22(3197-3203)
IEEE DOI 2212
Knowledge engineering, Training, Performance evaluation, Computational modeling, Self-supervised learning BibRef

Li, X.X.[Xiu-Xiu], Zhang, P.[Pu], Wang, C.X.[Chao-Xian], Wu, S.J.[Sheng-Jun],
Similarity Measurement Human Actions with GNN,
ICIVC22(825-830)
IEEE DOI 2301
Convolution, Feature extraction, Skeleton, Graph neural networks, Motion capture, Motion measurement, Reliability, human skeleton, LSTM BibRef

Shalmani, S.M.[Shervin Manzuri], Chiang, F.[Fei], Zheng, R.[Rong],
Efficient Action Recognition Using Confidence Distillation,
ICPR22(3362-3369)
IEEE DOI 2212
Training, Computational modeling, Neural networks, Machine learning, Predictive models, Sampling methods BibRef

Mao, Y.Y.[Yun-Yao], Zhou, W.G.[Wen-Gang], Lu, Z.B.[Zhen-Bo], Deng, J.J.[Jia-Jun], Li, H.Q.[Hou-Qiang],
CMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation,
ECCV22(III:734-752).
Springer DOI 2211
BibRef

Weng, Y.T.[Yue-Tian], Pan, Z.Z.[Zi-Zheng], Han, M.F.[Ming-Fei], Chang, X.J.[Xiao-Jun], Zhuang, B.[Bohan],
An Efficient Spatio-Temporal Pyramid Transformer for Action Detection,
ECCV22(XXXIV:358-375).
Springer DOI 2211
BibRef

Li, Z.[Zhi], He, L.[Lu], Xu, H.J.[Hui-Juan],
Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions,
ECCV22(X:567-584).
Springer DOI 2211
BibRef

Pu, S.[Shi], Zhao, K.[Kaili], Zheng, M.[Mao],
Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification,
CVPR22(19936-19945)
IEEE DOI 2210
Representation learning, Measurement, Analytical models, Codes, Computational modeling, Semantics, Action and event recognition, Video analysis and understanding BibRef

Lin, C.C.[Chung-Ching], Lin, K.[Kevin], Wang, L.J.[Li-Juan], Liu, Z.C.[Zi-Cheng], Li, L.J.[Lin-Jie],
Crossmodal Representation Learning for Zero-shot Action Recognition,
CVPR22(19946-19956)
IEEE DOI 2210
Training, Representation learning, Visualization, Text recognition, Computational modeling, Semantics, Benchmark testing, Video analysis and understanding BibRef

Truong, T.D.[Thanh-Dat], Bui, Q.H.[Quoc-Huy], Duong, C.N.[Chi Nhan], Seo, H.S.[Han-Seok], Phung, S.L.[Son Lam], Li, X.[Xin], Luu, K.[Khoa],
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition,
CVPR22(19998-20008)
IEEE DOI 2210
Correlation, Benchmark testing, Transformers, Robustness, Kinetic theory, Video analysis and understanding BibRef

Dai, R.[Rui], Das, S.[Srijan], Kahatapitiya, K.[Kumara], Ryoo, M.S.[Michael S.], Brémond, F.[François],
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection,
CVPR22(20009-20019)
IEEE DOI 2210
Fuses, Computational modeling, Benchmark testing, Task analysis, Mixers, Behavior analysis BibRef

Guo, H.J.[Hong-Ji], Wang, H.J.[Han-Jing], Ji, Q.[Qiang],
Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition,
CVPR22(20020-20029)
IEEE DOI 2210
Training, Analytical models, Uncertainty, Computational modeling, Training data, Predictive models, Transformers, Video analysis and understanding BibRef

Thatipelli, A.[Anirudh], Narayan, S.[Sanath], Khan, S.[Salman], Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], Ghanem, B.[Bernard],
Spatio-temporal Relation Modeling for Few-shot Action Recognition,
CVPR22(19926-19935)
IEEE DOI 2210
Representation learning, Codes, Computational modeling, Aggregates, Benchmark testing, retrieval, Recognition: detection BibRef

Khorasgani, S.H.[Salar Hosseini], Chen, Y.X.[Yu-Xuan], Shkurti, F.[Florian],
SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos,
CVPR22(16070-16080)
IEEE DOI 2210
Supervised learning, Self-supervised learning, Iterative methods, Videos, Self- semi- meta- Video analysis and understanding BibRef

Kumar, A.[Akash], Rawat, Y.S.[Yogesh Singh],
End-to-End Semi-Supervised Learning for Video Action Detection,
CVPR22(14680-14690)
IEEE DOI 2210
Location awareness, Codes, Annotations, Object segmentation, Semisupervised learning, Benchmark testing, Video analysis and understanding BibRef

Min, S.[Sunah], Moon, J.[Jinyoung],
Information Elevation Network for Online Action Detection and Anticipation,
Precognition22(2549-2557)
IEEE DOI 2210
Visualization, Surveillance, Color, Streaming media, Logic gates BibRef

Yang, L.J.[Li-Jin], Huang, Y.F.[Yi-Fei], Sugano, Y.[Yusuke], Sato, Y.[Yoichi],
Interact before Align: Leveraging Cross-Modal Knowledge for Domain Adaptive Action Recognition,
CVPR22(14702-14712)
IEEE DOI 2210
Adaptation models, Target recognition, Annotations, Semantics, Benchmark testing, Data models, Video analysis and understanding BibRef

Gong, X.Y.[Xin-Yu], Wang, H.[Heng], Shou, Z.[Zheng], Feiszli, M.[Matt], Wang, Z.Y.[Zhang-Yang], Yan, Z.C.[Zhi-Cheng],
Searching for Two-Stream Models in Multivariate Space for Video Recognition,
ICCV21(8013-8022)
IEEE DOI 2203
Computational modeling, Manuals, Streaming media, Benchmark testing, Space exploration, BibRef

Liu, B.[Bo], Li, H.X.[Hao-Xiang], Kang, H.[Hao], Hua, G.[Gang], Vasconcelos, N.M.[Nuno M.],
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition,
ICCV21(8189-8198)
IEEE DOI 2203
Training, Geometry, Transfer learning, Manuals, Sampling methods, Recognition and classification BibRef

Wang, X.[Xiang], Zhang, S.W.[Shi-Wei], Qing, Z.W.[Zhi-Wu], Shao, Y.J.[Yuan-Jie], Zuo, Z.R.[Zheng-Rong], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
OadTR: Online Action Detection with Transformers,
ICCV21(7545-7555)
IEEE DOI 2203
Training, Visualization, Recurrent neural networks, Streaming media, Transformers, Encoding, Spatiotemporal phenomena, Action and behavior recognition BibRef

Shi, B.F.[Bai-Feng], Dai, Q.[Qi], Hoffman, J.[Judy], Saenko, K.[Kate], Darrell, T.J.[Trevor J.], Xu, H.J.[Hui-Juan],
Temporal Action Detection with Multi-level Supervision,
ICCV21(8002-8012)
IEEE DOI 2203
Training, Costs, Annotations, Semisupervised learning, Feature extraction, Information filters, Motion and tracking BibRef

Li, R.[Rui], Zhang, Y.H.[Yi-Heng], Qiu, Z.F.[Zhao-Fan], Yao, T.[Ting], Liu, D.[Dong], Mei, T.[Tao],
Motion-Focused Contrastive Learning of Video Representations*,
ICCV21(2085-2094)
IEEE DOI 2203
How important is the actual motion in learning. Representation learning, Protocols, Feature extraction, Spatiotemporal phenomena, Motion measurement, BibRef

Qian, Y.J.[Yi-Jun], Kang, G.L.[Guo-Liang], Yu, L.J.[Li-Jun], Liu, W.H.[Wen-He], Hauptmann, A.G.[Alexander G.],
TRM: Temporal Relocation Module for Video Recognition,
Activity22(151-160)
IEEE DOI 2202
An approach to get video into neural network learning system. Adaptation models, Convolution, Computational modeling, Benchmark testing, Transmission line measurements BibRef

Degardin, B.[Bruno], Neves, J.C.[João C.], Lopes, V.[Vasco], Brito, J.[João], Yaghoubi, E.[Ehsan], Proença, H.[Hugo],
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis,
WACV22(2753-2762)
IEEE DOI 2202
Convolutional codes, Measurement, Shape, Stochastic processes, GANs BibRef

Leong, M.C.[Mei Chee], Tan, H.L.[Hui Li], Zhang, H.S.[Hao-Song], Li, L.Y.[Li-Yuan], Lin, F.[Feng], Lim, J.H.[Joo Hwee],
Joint Learning on the Hierarchy Representation for Fine-Grained Human Action Recognition,
ICIP21(1059-1063)
IEEE DOI 2201
Training, Image recognition, Image coding, Semantics, Task analysis, Action recognition, fine-grained action recognition, joint representation BibRef

Singh, A.[Ankit], Chakraborty, O.[Omprakash], Varshney, A.[Ashutosh], Panda, R.[Rameswar], Feris, R.S.[Rogerio S.], Saenko, K.[Kate], Das, A.[Abir],
Semi-Supervised Action Recognition with Temporal Contrastive Learning,
CVPR21(10384-10394)
IEEE DOI 2111
Image recognition, Semantics, Network architecture, Benchmark testing, Robustness BibRef

Wang, X.[Xiang], Zhang, S.W.[Shi-Wei], Qing, Z.W.[Zhi-Wu], Shao, Y.J.[Yuan-Jie], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal,
CVPR21(1905-1914)
IEEE DOI 2111
Codes, Perturbation methods, Proposals, Task analysis BibRef

Perrett, T.[Toby], Masullo, A.[Alessandro], Burghardt, T.[Tilo], Mirmehdi, M.[Majid], Damen, D.[Dima],
Temporal-Relational CrossTransformers for Few-Shot Action Recognition,
CVPR21(475-484)
IEEE DOI 2111
Prototypes, Kinetic theory, Spatiotemporal phenomena, Videos BibRef

Ben-Ari, R.[Rami], Nacson, M.S.[Mor Shpigel], Azulai, O.[Ophir], Barzelay, U.[Udi], Rotman, D.[Daniel],
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition,
LLID21(2780-2788)
IEEE DOI 2109
Training, Semantics, Benchmark testing, Extraterrestrial measurements BibRef

Rana, A.J.[Aayush J], Rawat, Y.S.[Yogesh S],
We don't Need Thousand Proposals: Single Shot Actor-Action Detection in Videos,
WACV21(2959-2968)
IEEE DOI 2106
Convolutional codes, Scalability, Computational modeling, Memory management, Proposals BibRef

Alibayev, M.[Maxat], Paulius, D.[David], Sun, Y.[Yu],
Developing Motion Code Embedding for Action Recognition in Videos,
ICPR21(7529-7536)
IEEE DOI 2105
Visualization, Taxonomy, Semantics, Neural networks, Machine learning, Feature extraction BibRef

Shao, J.[Jie], Xue, X.Y.[Xiang-Yang],
Learnable Higher-Order Representation for Action Recognition,
ICPR21(9038-9045)
IEEE DOI 2105
Visualization, Computational modeling, Architecture, Spatiotemporal phenomena, Kinetic theory BibRef

Roitberg, A.[Alina], Haurilet, M.[Monica], Martinez, M.[Manuel], Stiefelhagen, R.[Rainer],
Uncertainty-sensitive Activity Recognition: A Reliability Benchmark and the CARING Models,
ICPR21(3814-3821)
IEEE DOI 2105
Calibrated Action Recognition with Input Guidance. Temperature dependence, Uncertainty, Transforms, Activity recognition, Predictive models, Benchmark testing, Calibration BibRef

Gao, H.Y.[Hao-Yuan], Zhang, Y.F.[Yi-Fan], Sun, L.H.[Lin-Hui], Cheng, J.[Jian],
Action Representing by Constrained Conditional Mutual Information,
ACCV22(IV:291-306).
Springer DOI 2307
BibRef

Cheng, K.[Ke], Zhang, Y.F.[Yi-Fan], Li, C.H.[Cheng-Hua], Cheng, J.[Jian], Lu, H.Q.[Han-Qing],
Motion Complementary Network for Efficient Action Recognition,
ICPR21(1543-1549)
IEEE DOI 2105
Convolution, Graphics processing units, Throughput, Computational efficiency, Kinetic theory BibRef

Tsiaousis, M.[Michail], Burghouts, G.[Gertjan], Hillerström, F.[Fieke], van der Putten, P.[Peter],
Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection,
HCAU20(115-130).
Springer DOI 2103
BibRef

Li, H.S.[Hong-Sheng], Zhang, W.W.[Wei-Wei], Zhu, G.M.[Guang-Ming], Zhang, L.[Liang], Shen, P.[Peiyi], Song, J.[Juan],
Nonlinear Temporal Correlation Based Network for Action Recognition,
FBE20(748-763).
Springer DOI 2103
BibRef

Ma, H.D.[Han-Dong], Duan, L.X.[Li-Xin],
Temporal Action Proposal Generation via Multi-Task Feature Learning,
VCIP20(399-402)
IEEE DOI 2102
Proposals, Correlation, Feature extraction, Task analysis, Fuses, Convolution, correlation loss BibRef

Zhi, Y.[Yuan], Tong, Z.[Zhan], Wang, L.M.[Li-Min], Wu, G.S.[Gang-Shan],
MGSampler: An Explainable Sampling Strategy for Video Action Recognition,
ICCV21(1493-1502)
IEEE DOI 2203
Adaptation models, Codes, Motion segmentation, Computational modeling, Redundancy, Recognition and classification BibRef

Wang, Z.Z.[Zhen-Zhi], Gao, Z.T.[Zi-Teng], Wang, L.M.[Li-Min], Li, Z.F.[Zhi-Feng], Wu, G.S.[Gang-Shan],
Boundary-aware Cascade Networks for Temporal Action Segmentation,
ECCV20(XXV:34-51).
Springer DOI 2011
BibRef

Huang, X., Mousavi, H., Roig, G.,
Predictive Coding Networks Meet Action Recognition,
ICIP20(793-797)
IEEE DOI 2011
Feature extraction, Videos, Adaptive optics, Optical imaging, Optical network units, Training, Computational modeling, Optical flow BibRef

Weng, J.W.[Jun-Wu], Luo, D.H.[Dong-Hao], Wang, Y.B.[Ya-Biao], Tai, Y.[Ying], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Jiang, X.D.[Xu-Dong], Yuan, J.S.[Jun-Song],
Temporal Distinct Representation Learning for Action Recognition,
ECCV20(VII:363-378).
Springer DOI 2011
BibRef

Si, C.Y.[Chen-Yang], Nie, X.C.[Xue-Cheng], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu], Feng, J.S.[Jia-Shi],
Adversarial Self-supervised Learning for Semi-Supervised 3d Action Recognition,
ECCV20(VII:35-51).
Springer DOI 2011
BibRef

Piergiovanni, A.J., Angelova, A.[Anelia], Toshev, A.[Alexander], Ryoo, M.S.[Michael S.],
Adversarial Generative Grammars for Human Activity Prediction,
ECCV20(II:507-523).
Springer DOI 2011
BibRef

Bai, Y.R.[Yue-Ran], Wang, Y.Y.[Ying-Ying], Tong, Y.H.[Yun-Hai], Yang, Y.[Yang], Liu, Q.Y.[Qi-Yue], Liu, J.H.[Jun-Hui],
Boundary Content Graph Neural Network for Temporal Action Proposal Generation,
ECCV20(XXVIII:121-137).
Springer DOI 2011
BibRef

Long, T., Mettes, P.S., Shen, H.T., Snoek, C.G.M.,
Searching for Actions on the Hyperbole,
CVPR20(1138-1147)
IEEE DOI 2008
Prototypes, Image recognition, Standards, Mathematical model, Training, Semantics BibRef

Lin, T., Zhao, X., Fan, Z.,
Temporal action localization with two-stream segment-based RNN,
ICIP17(3400-3404)
IEEE DOI 1803
Bidirectional control, Feature extraction, Recurrent neural networks, Task analysis, Training, Videos, LSTM, RNN, Two-stream ConvNet BibRef

Xu, M., Gao, M., Chen, Y., Davis, L.S., Crandall, D.J.[David J.],
Temporal Recurrent Networks for Online Action Detection,
ICCV19(5531-5540)
IEEE DOI 2004
image motion analysis, recurrent neural nets, video signal processing, Temporal Recurrent Networks, Predictive models BibRef

Xu, M., Sharghi, A., Chen, X., Crandall, D.J.,
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition,
WACV18(1607-1615)
IEEE DOI 1806
feature extraction, image motion analysis, image recognition, image sequences, spatiotemporal phenomena, Videos BibRef

García, R.O.[Reinier Oves], Sucar, L.E.[L. Enrique],
What the Appearance Channel from Two-stream Architectures for Activity Recognition Is Learning?,
MCPR20(251-260).
Springer DOI 2007
BibRef

Biswas, S.[Sovan], Souri, Y., Gall, J.[Juergen],
Hierarchical Graph-RNNS for Action Detection of Multiple Activities,
ICIP19(1-5)
IEEE DOI 1910
Spatio-temporal action detection, Graph-RNN BibRef

Peng, W., Hong, X., Zhao, G.,
Video Action Recognition Via Neural Architecture Searching,
ICIP19(11-15)
IEEE DOI 1910
Automated machine learning, neural architecture search, video action recognition BibRef

Wang, H., Wang, L.,
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks,
CVPR17(3633-3642)
IEEE DOI 1711
Hidden Markov models, Logic gates, Recurrent neural networks, Skeleton, Videos BibRef

Khatir, N.[Nadjia], López-Sastre, R.J.[Roberto J.], Baptista-Ríos, M.[Marcos], Nait-Bahloul, S.[Safia], Acevedo-Rodríguez, F.J.[Francisco Javier],
Combining Online Clustering and Rank Pooling Dynamics for Action Proposals,
IbPRIA19(I:77-88).
Springer DOI 1910
BibRef

Esser, P.[Patrick], Haux, J.[Johannes], Milbich, T.[Timo], Ommer, B.[Björn],
Towards Learning a Realistic Rendering of Human Behavior,
HBU18(II:409-425).
Springer DOI 1905
BibRef

Kundu, J.N., Gor, M., Uppala, P.K., Radhakrishnan, V.B.,
Unsupervised Feature Learning of Human Actions As Trajectories in Pose Embedding Manifold,
WACV19(1459-1467)
IEEE DOI 1904
feature extraction, image motion analysis, image recognition, image representation, image sequences, Dynamics BibRef

Kim, T.S.[Tae Soo], Peven, M.[Mike], Qiu, W.C.[Wei-Chao], Yuille, A.L.[Alan L.], Hager, G.D.[Gregory D.],
Synthesizing Attributes with Unreal Engine for Fine-grained Activity Analysis,
HADCV19(35-37)
IEEE DOI 1902
Training with simulated data. Automobiles, Visualization, Geometry, Engines, Training, Cameras BibRef

Khalid, M.U., Yu, J.,
Multi-Modal Three-Stream Network for Action Recognition,
ICPR18(3210-3215)
IEEE DOI 1812
Tensile stress, Interpolation, Feature extraction, Training BibRef

Zhu, J., Zou, W., Zhu, Z.,
Two-Stream Gated Fusion ConvNets for Action Recognition,
ICPR18(597-602)
IEEE DOI 1812
Logic gates, Videos, Training, Feature extraction, Fuses, Learning systems BibRef

Wang, Y., Zhou, L., Qiao, Y.,
Temporal Hallucinating for Action Recognition with Few Still Images,
CVPR18(5314-5322)
IEEE DOI 1812
Videos, Training, Image recognition, Memory modules, Optical imaging, Kernel BibRef

Coskun, H.[Huseyin], Zareian, A.[Alireza], Moore, J.L.[Joshua L.], Tombari, F.[Federico], Wang, C.[Chen],
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning,
ECCV22(XXXI:1-22).
Springer DOI 2211
BibRef

Cai, H.Y.[Hao-Ye], Bai, C.Y.[Chun-Yan], Tai, Y.W.[Yu-Wing], Tang, C.K.[Chi-Keung],
Deep Video Generation, Prediction and Completion of Human Action Sequences,
ECCV18(II: 374-390).
Springer DOI 1810
BibRef

Andonian, A.[Alex], Fosco, C.[Camilo], Monfort, M.[Mathew], Lee, A.[Allen], Feris, R.S.[Rogerio S.], Vondrick, C.[Carl], Oliva, A.[Aude],
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos,
ECCV20(XVIII:18-34).
Springer DOI 2012
BibRef

Zhou, B.[Bolei], Andonian, A.[Alex], Oliva, A.[Aude], Torralba, A.B.[Antonio B.],
Temporal Relational Reasoning in Videos,
ECCV18(I: 831-846).
Springer DOI 1810
learn and reason about temporal dependencies between video frames BibRef

Wang, L.[Lan], Gao, C.Q.[Chen-Qiang], Yang, L.[Luyu], Zhao, Y.[Yue], Zuo, W.M.[Wang-Meng], Meng, D.Y.[De-Yu],
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-Modalities,
ECCV18(VI: 389-406).
Springer DOI 1810
BibRef

Qiao, X., Zhou, C., Xu, C., Cui, Z., Yang, J.,
Action Recognition with Spatial-Temporal Representation Analysis Across Grassmannian Manifold and Euclidean Space,
ICIP18(3448-3452)
IEEE DOI 1809
Manifolds, Feature extraction, Trajectory, Support vector machines, Measurement, Task analysis, Neural networks, Action recognition, Temporal information BibRef

Zhang, T., Li, N., Huang, J., Zhong, J., Li, G.,
An Active Action Proposal Method Based on Reinforcement Learning,
ICIP18(4053-4057)
IEEE DOI 1809
Proposals, Training, Task analysis, Feature extraction, Learning (artificial intelligence), Q-learning BibRef

Mehrseresht, N.[Nagita],
Action Parsing Using Context Features,
DICTA17(1-7)
IEEE DOI 1804
dynamic programming, feature extraction, image classification, image segmentation, image sequences, program compilers, Video sequences BibRef

Pan, R., Ma, L., Zhan, Y., Cai, S.,
A Novel Orientation-Context Descriptor and Locality-Preserving Fisher Discrimination Dictionary Learning for Action Recognition,
DICTA17(1-8)
IEEE DOI 1804
feature extraction, image coding, image motion analysis, image recognition, image representation, FDDL method, Training BibRef

Ge, R.Z.[Run-Zhou], Gao, J.Y.[Ji-Yang], Chen, K.[Kan], Nevatia, R.[Ram],
MAC: Mining Activity Concepts for Language-Based Temporal Localization,
WACV19(245-253)
IEEE DOI 1904
data mining, image representation, learning (artificial intelligence), Task analysis BibRef

Gao, J.Y.[Ji-Yang], Chen, K.[Kan], Nevatia, R.[Ram],
CTAP: Complementary Temporal Action Proposal Generation,
ECCV18(II: 70-85).
Springer DOI 1810
BibRef

Gao, J.Y.[Ji-Yang], Yang, Z.H.[Zhen-Heng], Sun, C.[Chen], Chen, K.[Kan], Nevatia, R.[Ram],
TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals,
ICCV17(3648-3656)
IEEE DOI 1802
Predicts action proposals and refines the temporal boundaries. Unit feature reuse. feature extraction, gesture recognition, image classification, image colour analysis, image motion analysis, object detection, Windows BibRef

Yang, J., Yuan, J.,
Common Action Discovery and Localization in Unconstrained Videos,
ICCV17(2176-2185)
IEEE DOI 1802
computational complexity, graph theory, optimisation, video signal processing, affinity graph, common action discovery, Videos BibRef

Yeung, S.[Serena], Ramanathan, V.[Vignesh], Russakovsky, O.[Olga], Shen, L.Y.[Li-Yue], Mori, G.[Greg], Fei-Fei, L.[Li],
Learning to Learn from Noisy Web Videos,
CVPR17(7455-7463)
IEEE DOI 1711
Animals, Data models, Labeling, Noise measurement, Training, Videos, Visualization BibRef

Shi, Z., Kim, T.K.,
Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences,
CVPR17(4684-4693)
IEEE DOI 1711
Data models, Feature extraction, Hidden Markov models, Recurrent neural networks, Skeleton, Training BibRef

Choi, J., Sharma, G., Chandraker, M., Huang, J.,
Unsupervised and Semi-Supervised Domain Adaptation for Action Recognition from Drones,
WACV20(1706-1715)
IEEE DOI 2006
Videos, Drones, Task analysis, Training, Adaptation models, Feature extraction, Optimization BibRef

Yan, X., Hu, S., Ye, Y.,
Multi-task Clustering of Human Actions by Sharing Information,
CVPR17(4049-4057)
IEEE DOI 1711
Clustering methods, Correlation, Linear programming, Mutual information, Videos, Vocabulary BibRef

Gao, J.Y.[Ji-Yang], Nevatia, R.[Ram],
Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Data,
ACCV16(II: 19-34).
Springer DOI 1704
BibRef

Dayrit, F.L.[Fabian Lorenzo], Kimura, R.[Ryosuke], Nakashima, Y.[Yuta], Blanco, A.[Ambrosio], Kawasaki, H.[Hiroshi], Ikeuchi, K.[Katsushi], Sato, T.[Tomokazu], Yokoya, N.[Naokazu],
ReMagicMirror: Action Learning Using Human Reenactment with the Mirror Metaphor,
MMMod17(I: 303-315).
Springer DOI 1701
BibRef

Yeung, S., Russakovsky, O., Mori, G., Fei-Fei, L.[Li],
End-to-End Learning of Action Detection from Frame Glimpses in Videos,
CVPR16(2678-2687)
IEEE DOI 1612
BibRef

Singh, B., Marks, T.K., Jones, M., Tuzel, O.[Oncel], Shao, M.,
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection,
CVPR16(1961-1970)
IEEE DOI 1612
BibRef

Misra, I.[Ishan], Zitnick, C.L.[C. Lawrence], Hebert, M.[Martial],
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification,
ECCV16(I: 527-544).
Springer DOI 1611
Action recognition BibRef

Yu, T.S.[Tian-Shu], Li, Y.K.[Yi-Kang], Li, B.X.[Bao-Xin],
Rhyrnn: Rhythmic RNN for Recognizing Events in Long and Complex Videos,
ECCV20(X:127-144).
Springer DOI 2011
BibRef

Cai, J.J.[Jun-Jie], Yu, J.[Jie], Imai, F.[Francisco], Tian, Q.[Qi],
Towards temporal adaptive representation for video action recognition,
ICIP16(4155-4159)
IEEE DOI 1610
Computational modeling BibRef

Chen, J.L.[Jia-Lin], Lin, Z.Y.[Zhi-Yi], Wan, Y.C.[Yi-Chen], Chen, L.G.[Liang-Gee],
Accelerated local feature extraction in a reuse scheme for efficient action recognition,
ICIP16(296-299)
IEEE DOI 1610
Acceleration BibRef

Park, E.[Eunbyung], Berg, A.C.[Alexander C.],
Meta-tracker: Fast and Robust Online Adaptation for Visual Object Trackers,
ECCV18(III: 587-604).
Springer DOI 1810
BibRef

Bagheri, M.A., Gao, Q., Escalera, S.,
Support vector machines with time series distance kernels for action classification,
WACV16(1-7)
IEEE DOI 1606
Kernel BibRef

Bozorgtabar, B.[Behzad], Goecke, R.[Roland],
Multi-level action detection via learning latent structure,
ICIP15(3004-3008)
IEEE DOI 1512
Action detection BibRef

Lee, H.Y.[Hsin-Ying], Huang, J.B.[Jia-Bin], Singh, M.[Maneesh], Yang, M.H.[Ming-Hsuan],
Unsupervised Representation Learning by Sorting Sequences,
ICCV17(667-676)
IEEE DOI 1802
Learn the temporal sequence. feature extraction, image classification, image representation, image sequences, neural nets, object detection, sorting, Visualization BibRef

Ji, J., Cao, K., Niebles, J.C.[Juan Carlos],
Learning Temporal Action Proposals With Fewer Labels,
ICCV19(7072-7081)
IEEE DOI 2004
image motion analysis, image sequences, object detection, supervised learning, video signal processing, BibRef

Tahmoush, D.[David], Bonial, C.,
Applying attributes to improve human activity recognition,
AIPR15(1-4)
IEEE DOI 1605
feature extraction BibRef

Tahmoush, D.[David],
Applying action attribute class validation to improve human activity recognition,
ChaLearn15(15-21)
IEEE DOI 1510
Accuracy; Databases; Joints; Ontologies; Training; Training data. Dealing with noisy training data for actions. BibRef

Ramanathan, V.[Vignesh], Tang, K.[Kevin], Mori, G.[Greg], Fei-Fei, L.[Li],
Learning Temporal Embeddings for Complex Video Analysis,
ICCV15(4471-4479)
IEEE DOI 1602
Coherence BibRef

Ramanathan, V.[Vignesh], Li, C.C.[Cong-Cong], Deng, J.[Jia], Han, W.[Wei], Li, Z.[Zhen], Gu, K.L.[Kun-Long], Song, Y.[Yang], Bengio, S.[Samy], Rossenberg, C.[Chuck], Fei-Fei, L.[Li],
Learning semantic relationships for better action retrieval in images,
CVPR15(1100-1109)
IEEE DOI 1510
BibRef

Wu, X.Q.[Xu-Qing], Shah, S.K.[Shishir K.],
Regularized Multi-view Multi-metric Learning for Action Recognition,
ICPR14(471-476)
IEEE DOI 1412
Cameras BibRef

Wu, Z.M.[Zi-Ming], Ng, W.W.Y.[Wing W.Y.],
Human action recognition using action bank and RBFNN trained by L-GEM,
ICWAPR14(30-35)
IEEE DOI 1402
BibRef

Jones, S.[Simon], Shao, L.[Ling],
Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context,
CVPR14(604-611)
IEEE DOI 1409
Human Action Analysis; Unsupervised Learning; Video Clustering BibRef

Chen, Y.B.[Yuan-Bo], Guo, X.[Xin],
Learning non-negative locality-constrained Linear Coding for human action recognition,
VCIP13(1-6)
IEEE DOI 1402
behavioural sciences computing BibRef

lo Presti, L.[Liliana], Sclaroff, S.[Stan], Rozga, A.,
Joint Alignment and Modeling of Correlated Behavior Streams,
SocialInter13(730-737)
IEEE DOI 1403
correlation methods BibRef

Cai, Q.[Qiao], Yin, Y.F.[Ya-Feng], Man, H.[Hong],
Learning spatio-temporal dependencies for action recognition,
ICIP13(3740-3744)
IEEE DOI 1402
Spatio-temporal dependencies; action recognition; self-organizing map BibRef

Zhou, W.[Wen], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua], Zhang, Z.[Zhong], Ma, L.[Long],
Learning weighted features for human action recognition,
ICPR12(1160-1163).
WWW Link. 1302
BibRef

Baccouche, M.[Moez], Mamalet, F.[Franck], Wolf, C.[Christian], Garcia, C.[Christophe], Baskurt, A.[Atilla],
Sparse shift-invariant representation of local 2D patterns and sequence learning for human action recognition,
ICPR12(3823-3826).
WWW Link. 1302
BibRef

Wolf, C.[Christian], Baskurt, A.[Atilla],
Action recognition in videos,
IPTA12(3-4)
IEEE DOI 1503
graph theory BibRef

Possegger, H.[Horst], Mauthner, T.[Thomas], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Occlusion Geodesics for Online Multi-object Tracking,
CVPR14(1306-1313)
IEEE DOI 1409
Multi-Object Tracking;Occlusion Geodesics;Online Tracking BibRef

Mauthner, T.[Thomas], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Learn to Move: Activity Specific Motion Models for Tracking by Detection,
ARTEMIS12(III: 183-192).
Springer DOI 1210
BibRef

Roth, P.M.[Peter M.], Mauthner, T.[Thomas], Khan, I.[Inayatullah], Bischof, H.[Horst],
Efficient human action recognition by cascaded linear classifcation,
ObjectEvent09(546-553).
IEEE DOI 0910
BibRef

Mauthner, T.[Thomas], Roth, P.M.[Peter M.], Bischof, H.[Horst],
Temporal Feature Weighting for Prototype-Based Action Recognition,
ACCV10(II: 566-579).
Springer DOI 1011
BibRef
Earlier:
Instant Action Recognition,
SCIA09(1-10).
Springer DOI 0906
BibRef

Ablavsky, V.[Vitaly], Sclaroff, S.[Stan],
Learning parameterized histogram kernels on the simplex manifold for image and action classification,
ICCV11(1473-1480).
IEEE DOI 1201

See also Layered Graphical Models for Tracking Partially Occluded Objects. BibRef

Kovashka, A.[Adriana], Grauman, K.[Kristen],
Learning a hierarchy of discriminative space-time neighborhood features for human action recognition,
CVPR10(2046-2053).
IEEE DOI 1006
BibRef

Yao, B.[Benjamin], Zhu, S.C.[Song-Chun],
Learning deformable action templates from cluttered videos,
ICCV09(1507-1514).
IEEE DOI 0909
Sequence of image templates with shape and motion primitives (Gabor wavelets, and optical flow). BibRef

Connolly, C.I.[Christopher I.],
Learning to Recognize Complex Actions Using Conditional Random Fields,
ISVC07(II: 340-348).
Springer DOI 0711
BibRef

Jung, S.H.[Sang-Hack], Guo, Y.L.[Yan-Lin], Sawhney, H.S.[Harpreet S.], Kumar, R.T.[Rakesh T.],
Action exemplar based real-time action detection,
MLMotion09(498-505).
IEEE DOI 0910
BibRef
Earlier:
Multiple Cue Integrated Action Detection,
CVHCI07(108-117).
Springer DOI 0710
BibRef

Yang, C.J.[Chang-Jiang], Guo, Y.L.[Yan-Lin], Sawhney, H.S.[Harpreet S.], Kumar, R.T.[Rakesh T.],
Learning Actions Using Robust String Kernels,
HUMO07(313-327).
Springer DOI 0710
BibRef

Lee, C.,
Learning Reduced-Dimension Models of Human Actions,
CMU-RI-TR-00-17, May 2000. BibRef 0005 Ph.D.Thesis.
PDF File. 0102
BibRef

Bobick, A.F.[Aaron F.], Pentland, A.P.[Alex P.], Poggio, T.[Tommy],
VSAM at the MIT Media Laboratory and CBCL: Learning and Understanding Action in Video Imagery PI Report 1998,
DARPA98(85-91). BibRef 9800
And:
VSAM at the MIT Media laboratory and CBCL: Learning and Understanding Action in Video Imagery,
DARPA97(25-30). BibRef

Feichtenhofer, C.[Christoph], Fan, H., Malik, J., He, K.,
SlowFast Networks for Video Recognition,
ICCV19(6201-6210)
IEEE DOI 2004
Code, Video Processing.
WWW Link. image capture, image classification, image motion analysis, learning (artificial intelligence), video signal processing, Channel capacity BibRef

Feichtenhofer, C.[Christoph], Pinz, A.[Axel], Wildes, R.P.[Richard P.],
Dynamically encoded actions based on spacetime saliency,
CVPR15(2755-2764)
IEEE DOI 1510
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Deep Networks, Deep Learning for Human Action Recognition .