Hasan, M.[Mahmudul],
Roy-Chowdhury, A.K.[Amit K.],
A Continuous Learning Framework for Activity Recognition Using Deep
Hybrid Feature Models,
MultMed(17), No. 11, November 2015, pp. 1909-1922.
IEEE DOI
1511
BibRef
And:
Context Aware Active Learning of Activity Recognition Models,
ICCV15(4543-4551)
IEEE DOI
1602
BibRef
Earlier:
Incremental Activity Modeling and Recognition in Streaming Videos,
CVPR14(796-803)
IEEE DOI
1409
BibRef
And:
Continuous Learning of Human Activity Models Using Deep Nets,
ECCV14(III: 705-720).
Springer DOI
1408
Adaptation models.
Computational modeling
BibRef
Hasan, M.[Mahmudul],
Roy-Chowdhury, A.K.[Amit K.],
Incremental learning of human activity models from videos,
CVIU(144), No. 1, 2016, pp. 24-35.
Elsevier DOI
1604
Incremental learning
BibRef
Hasan, M.[Mahmudul],
Paul, S.,
Mourikis, A.I.,
Roy-Chowdhury, A.K.[Amit K.],
Context-Aware Query Selection for Active Learning in Event
Recognition,
PAMI(42), No. 3, March 2020, pp. 554-567.
IEEE DOI
2002
Labeling, Activity recognition, Context modeling, Streaming media,
Feature extraction, Entropy, Manuals, Active learning,
information theory
BibRef
Mahmud, T.[Tahmida],
Billah, M.[Mohammad],
Hasan, M.[Mahmudul],
Roy-Chowdhury, A.K.[Amit K.],
Prediction and Description of Near-Future Activities in Video,
CVIU(210), 2021, pp. 103230.
Elsevier DOI
2109
Label, Caption, LSTM, Fully connected network, Sequence-to-sequence
BibRef
Mahmud, T.[Tahmida],
Hasan, M.[Mahmudul],
Chakraborty, A.[Anirban],
Roy-Chowdhury, A.K.[Amit K.],
A poisson process model for activity forecasting,
ICIP16(3339-3343)
IEEE DOI
1610
Forecasting what comes next.
BibRef
Mahmud, T.[Tahmida],
Hasan, M.[Mahmudul],
Roy-Chowdhury, A.K.[Amit K.],
Joint Prediction of Activity Labels and Starting Times in Untrimmed
Videos,
ICCV17(5784-5793)
IEEE DOI
1802
image representation, image sequences, object detection,
video signal processing,
Visualization
BibRef
Liu, L.,
Zhou, Y.,
Shao, L.,
Deep Action Parsing in Videos With Large-Scale Synthesized Data,
IP(27), No. 6, June 2018, pp. 2869-2882.
IEEE DOI
1804
learning (artificial intelligence), neural nets,
video signal processing, DAP3D, NASA data set,
attributes learning
BibRef
Liu, J.,
Wang, G.,
Duan, L.Y.,
Abdiyeva, K.,
Kot, A.C.,
Skeleton-Based Human Action Recognition With Global Context-Aware
Attention LSTM Networks,
IP(27), No. 4, April 2018, pp. 1586-1599.
IEEE DOI
1802
feature extraction, gesture recognition, image motion analysis,
image representation, image sequences, recurrent neural nets,
skeleton sequence
BibRef
Liu, J.,
Wang, G.,
Hu, P.,
Duan, L.Y.,
Kot, A.C.,
Global Context-Aware Attention LSTM Networks for 3D Action
Recognition,
CVPR17(3671-3680)
IEEE DOI
1711
Data models, Hidden Markov models, Logic gates, Reliability,
Skeleton, Solid modeling,
BibRef
Redondo-Cabrera, C.[Carolina],
Lopez-Sastre, R.J.[Roberto J.],
Unsupervised learning from videos using temporal coherency deep
networks,
CVIU(179), 2019, pp. 79-89.
Elsevier DOI
1903
Unsupervised learning, Action discovery, Action recognition,
Object recognition, Deep learning
BibRef
Wang, Y.Y.[Ying-Ying],
Li, W.[Wei],
Tao, R.[Ran],
Multi-Branch Spatial-Temporal Network for Action Recognition,
SPLetters(26), No. 10, October 2019, pp. 1556-1560.
IEEE DOI
1909
Videos, Feature extraction, Biological system modeling, Deep learning,
long-term feature layer
BibRef
Quan, Y.H.[Yu-Hui],
Chen, Y.X.[Yi-Xin],
Xu, R.T.[Ruo-Tao],
Ji, H.[Hui],
Attention with structure regularization for action recognition,
CVIU(187), 2019, pp. 102794.
Elsevier DOI
1909
Action recognition, Attention, Block-wise sparsity, Deep recurrent network
BibRef
Gu, Y.[Ye],
Ye, X.F.[Xiao-Feng],
Sheng, W.H.[Wei-Hua],
Ou, Y.S.[Yong-Sheng],
Li, Y.Q.[Yong-Qiang],
Multiple stream deep learning model for human action recognition,
IVC(93), 2020, pp. 103818.
Elsevier DOI
2001
Deep learning, Information fusion, Action recognition
BibRef
Lavinia, Y.[Yukhe],
Vo, H.[Holly],
Verma, A.[Abhishek],
New colour fusion deep learning model for large-scale action
recognition,
IJCVR(10), No. 1, 2020, pp. 41-60.
DOI Link
2001
BibRef
Liu, Y.,
Lu, Z.,
Li, J.,
Yang, T.,
Yao, C.,
Deep Image-to-Video Adaptation and Fusion Networks for Action
Recognition,
IP(29), 2020, pp. 3168-3182.
IEEE DOI
2002
Action recognition, adaptation, deep learning, fusion
BibRef
Gao, P.J.[Pei-Jun],
Zhao, D.[Dan],
Chen, X.A.[Xuan-Ang],
Multi-dimensional data modelling of video image action recognition and
motion capture in deep learning framework,
IET-IPR(14), No. 7, 29 May 2020, pp. 1257-1264.
DOI Link
2005
BibRef
Garcia, N.C.[Nuno C.],
Morerio, P.[Pietro],
Murino, V.[Vittorio],
Learning with Privileged Information via Adversarial Discriminative
Modality Distillation,
PAMI(42), No. 10, October 2020, pp. 2581-2593.
IEEE DOI
2009
BibRef
Earlier:
Modality Distillation with Multiple Stream Networks for Action
Recognition,
ECCV18(VIII: 106-121).
Springer DOI
1810
Task analysis, Training, Videos, Data models, Analytical models,
Deep learning, modality hallucination
BibRef
Zhang, J.,
Hu, H.,
Liu, Z.,
Appearance-and-Dynamic Learning With Bifurcated Convolution Neural
Network for Action Recognition,
CirSysVideo(31), No. 4, April 2021, pp. 1593-1606.
IEEE DOI
2104
Solid modeling, Deep learning,
Feature extraction, Convolution, convolutional neural network
BibRef
Liu, Y.[Yu],
Yang, F.[Fan],
Ginhac, D.[Dominique],
ACDnet: An action detection network for real-time edge computing
based on flow-guided feature approximation and memory aggregation,
PRL(145), 2021, pp. 118-126.
Elsevier DOI
2104
Action detection, Real-time video processing, Edge computing,
Motion-guided features, Deep learning
BibRef
Hassan, E.[Ehtesham],
Learning Video Actions in Two Stream Recurrent Neural Network,
PRL(151), 2021, pp. 200-208.
Elsevier DOI
2110
Action recognition, Two-stream deep network, LSTM, Feature fusion
Recurrent neural network.
BibRef
Zhang, G.W.[Guan-Wen],
Rao, Y.K.[Yu-Kun],
Wang, C.H.[Chang-Hao],
Zhou, W.[Wei],
Ji, X.Y.[Xiang-Yang],
A deep learning method for video-based action recognition,
IET-IPR(15), No. 14, 2021, pp. 3498-3511.
DOI Link
2112
BibRef
Chen, L.[Lei],
Lu, J.W.[Ji-Wen],
Song, Z.J.[Zhan-Jie],
Zhou, J.[Jie],
Ambiguousness-Aware State Evolution for Action Prediction,
CirSysVideo(32), No. 9, September 2022, pp. 6058-6072.
IEEE DOI
2209
BibRef
Earlier:
Part-Activated Deep Reinforcement Learning for Action Prediction,
ECCV18(III: 435-451).
Springer DOI
1810
Videos, Uncertainty, Skeleton, Task analysis, Semantics,
Predictive models, Pipelines, Action prediction, skeleton
BibRef
Chen, L.[Lei],
Song, Z.J.[Zhan-Jie],
Frame-part-activated deep reinforcement learning for Action
Prediction,
PRL(180), 2024, pp. 113-119.
Elsevier DOI
2404
Action prediction, Deep reinforcement learning, Skeleton, Part model
BibRef
Vahdani, E.[Elahe],
Tian, Y.L.[Ying-Li],
Deep Learning-Based Action Detection in Untrimmed Videos: A Survey,
PAMI(45), No. 4, April 2023, pp. 4302-4320.
IEEE DOI
2303
Videos, Task analysis, Proposals, Training, Annotations, Automobiles,
Urban areas, Action understanding, temporal action detection,
full and limited supervision
BibRef
Sheng, B.[Biyun],
Xiao, F.[Fu],
Gui, L.Q.[Lin-Qing],
Guo, Z.X.[Zheng-Xin],
Context-Aware Faster RCNN for CSI-Based Human Action Perception,
HMS(53), No. 2, April 2023, pp. 438-448.
IEEE DOI
2303
Feature extraction, Proposals, Task analysis, Sensors,
Computational modeling, Wireless sensor networks, Deep learning,
faster RCNN
BibRef
Jiang, B.[Bo],
Yu, J.H.[Jia-Hong],
Zhou, L.[Lei],
Wu, K.[Kailin],
Yang, Y.[Yang],
Two-Pathway Transformer Network for Video Action Recognition,
ICIP21(1089-1093)
IEEE DOI
2201
Image recognition, Face recognition, Computational modeling,
Neural networks, Information representation, Decoding, deep learning
BibRef
Voillemin, T.[Théo],
Wannous, H.[Hazem],
Vandeborre, J.P.[Jean-Philippe],
2D Deep Video Capsule Network with Temporal Shift for Action
Recognition,
ICPR21(3513-3519)
IEEE DOI
2105
Image recognition, Video sequences,
Streaming media, Network architecture, Real-time systems
BibRef
Memmesheimer, R.[Raphael],
Häring, S.[Simon],
Theisen, N.[Nick],
Paulus, D.[Dietrich],
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action
Recognition,
WACV22(837-845)
IEEE DOI
2202
BibRef
Earlier: A1, A3, A4, Only:
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot
Action Recognition,
ICPR21(4573-4580)
IEEE DOI
2105
Measurement, Training, Image recognition, Protocols, Semantics,
Human-robot interaction, Euclidean distance,
Action and Behavior Recognition Biometrics -> Gesture Recognition.
Training, Wrist, Protocols, Shape, Training data, Transforms,
Nearest neighbor methods
BibRef
Alati, E.,
Mauro, L.,
Ntouskos, V.,
Pirri, F.,
Help by Predicting What to Do,
ICIP19(1930-1934)
IEEE DOI
1910
Deep learning, activity recognition, action recognition,
scene segmentation, need for help recognition, human-robot collaboration
BibRef
Coskun, H.[Huseyin],
Tan, D.J.[David Joseph],
Conjeti, S.[Sailesh],
Navab, N.[Nassir],
Tombari, F.[Federico],
Human Motion Analysis with Deep Metric Learning,
ECCV18(XIV: 693-710).
Springer DOI
1810
BibRef
Büchler, U.[Uta],
Brattoli, B.[Biagio],
Ommer, B.[Björn],
Improving Spatiotemporal Self-supervision by Deep Reinforcement
Learning,
ECCV18(XV: 797-814).
Springer DOI
1810
BibRef
de Souza, C.R.[César Roberto],
Gaidon, A.[Adrien],
Cabon, Y.[Yohann],
López, A.M.[Antonio Manuel],
Procedural Generation of Videos to Train Deep Action Recognition
Networks,
CVPR17(2594-2604)
IEEE DOI
1711
Animation, Cameras, Games, Machine learning, Manuals, Training, Videos
BibRef
Kong, Y.,
Tao, Z.,
Fu, Y.,
Deep Sequential Context Networks for Action Prediction,
CVPR17(3662-3670)
IEEE DOI
1711
Data mining, Feature extraction, Robustness, Testing,
Training, Videos, Visualization
BibRef
Rahman, M.A.[M. Atiqur],
Wang, Y.[Yang],
Optimizing Intersection-Over-Union in Deep Neural Networks for Image
Segmentation,
ISVC16(I: 234-244).
Springer DOI
1701
BibRef
And:
Learning Neural Networks with Ranking-Based Losses for Action
Retrieval,
CRV16(1-7)
IEEE DOI
1612
Award, Best Vision Paper. ROC area optimization; deep learning; image/video retrieval
BibRef
Zhu, W.J.[Wang-Jiang],
Hu, J.[Jie],
Sun, G.[Gang],
Cao, X.D.[Xu-Dong],
Qiao, Y.[Yu],
A Key Volume Mining Deep Framework for Action Recognition,
CVPR16(1991-1999)
IEEE DOI
1612
BibRef
Lin, Z.H.[Zhi-Hui],
Yuan, C.[Chun],
A Very Deep Sequences Learning Approach for Human Action Recognition,
MMMod16(II: 256-267).
Springer DOI
1601
BibRef
di Nardo, E.[Emanuel],
Petrosino, A.[Alfredo],
Ullah, I.[Ihsan],
EmoP3D: A Brain Like Pyramidal Deep Neural Network for Emotion
Recognition,
BrainDriven18(III:607-616).
Springer DOI
1905
BibRef
Ullah, I.[Ihsan],
Petrosino, A.[Alfredo],
A Spatio-temporal Feature Learning Approach for Dynamic Scene
Recognition,
PReMI17(591-598).
Springer DOI
1711
BibRef
Earlier:
Spatiotemporal Features Learning with 3DPyraNet,
ACIVS16(638-647).
Springer DOI
1611
BibRef
And:
A Strict Pyramidal Deep Neural Network for Action Recognition,
CIAP15(I:236-245).
Springer DOI
1511
Actions.
BibRef
Jhuo, I.H.[I-Hong],
Lee, D.T.,
Video Event Detection via Multi-modality Deep Learning,
ICPR14(666-671)
IEEE DOI
1412
Event detection. Audio/video.
BibRef
Sun, L.[Lin],
Jia, K.[Kui],
Chan, T.H.[Tsung-Han],
Fang, Y.Q.[Yu-Qiang],
Wang, G.[Gang],
Yan, S.C.[Shui-Cheng],
DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition,
CVPR14(2625-2632)
IEEE DOI
1409
action recognition; deep learning; slow feature analysis
See also Slow Feature Analysis for Human Action Recognition.
BibRef
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Incremental Learning for Human Action Recognition .