Yeo, C.,
Ahammad, P.,
Ramchandran, K.,
Sastry, S.S.,
High-Speed Action Recognition and Localization in Compressed Domain
Videos,
CirSysVideo(18), No. 8, August 2008, pp. 1006-1015.
IEEE DOI
0809
BibRef
Cho, J.C.[Jung-Chan],
Lee, M.[Minsik],
Chang, H.J.[Hyung Jin],
Oh, S.H.[Song-Hwai],
Robust action recognition using local motion and group sparsity,
PR(47), No. 5, 2014, pp. 1813-1825.
Elsevier DOI
1402
Action recognition
BibRef
Wang, G.F.[Guo-Feng],
Qin, X.Y.[Xue-Ying],
Zhong, F.[Fan],
Liu, Y.[Yue],
Li, H.B.[Hong-Bo],
Peng, Q.S.[Qun-Sheng],
Yang, M.H.[Ming-Hsuan],
Visual Tracking via Sparse and Local Linear Coding,
IP(24), No. 11, November 2015, pp. 3796-3809.
IEEE DOI
1509
image coding
BibRef
Earlier: A1, A3, A4, A6, A2, Only:
Visual Tracking in Continuous Appearance Space via Sparse Coding,
ACCV12(III:57-70).
Springer DOI
1304
See also Visual Tracking via Temporally Smooth Sparse Coding.
See also Visual Tracking via Coarse and Fine Structural Local Sparse Appearance Models.
BibRef
Qi, Y.K.[Yuan-Kai],
Qin, L.[Lei],
Zhang, J.[Jian],
Zhang, S.P.[Sheng-Ping],
Huang, Q.M.[Qing-Ming],
Yang, M.H.[Ming-Hsuan],
Structure-Aware Local Sparse Coding for Visual Tracking,
IP(27), No. 8, August 2018, pp. 3857-3869.
IEEE DOI
1806
image coding, image representation, image sequences,
object tracking, target tracking, dictionary,
template update
BibRef
Jain, M.[Mihir],
van Gemert, J.[Jan],
Jégou, H.[Hervé],
Bouthemy, P.[Patrick],
Snoek, C.G.M.[Cees G. M.],
Tubelets: Unsupervised Action Proposals from Spatiotemporal
Super-Voxels,
IJCV(124), No. 3, September 2017, pp. 287-311.
Springer DOI
1708
BibRef
Earlier:
Action Localization with Tubelets from Motion,
CVPR14(740-747)
IEEE DOI
1409
determine when and where certain actions appear.
BibRef
Jain, M.[Mihir],
van Gemert, J.C.[Jan C.],
Snoek, C.G.M.[Cees G.M.],
What do 15,000 object categories tell us about classifying and
localizing actions?,
CVPR15(46-55)
IEEE DOI
1510
BibRef
Mettes, P.S.[Pascal S.],
Snoek, C.G.M.[Cees G. M.],
Spatial-Aware Object Embeddings for Zero-Shot Localization and
Classification of Actions,
ICCV17(4453-4462)
IEEE DOI
1802
image classification, image motion analysis, object detection,
object recognition, video signal processing,
Trajectory
BibRef
Mettes, P.S.[Pascal S.],
Snoek, C.G.M.[Cees G. M.],
Pointly-Supervised Action Localization,
IJCV(127), No. 3, March 2019, pp. 263-281.
Springer DOI
1903
Localization by finding bounding boxes.
BibRef
Mettes, P.S.[Pascal S.],
van Gemert, J.C.[Jan C.],
Snoek, C.G.M.[Cees G. M.],
Spot On: Action Localization from Pointly-Supervised Proposals,
ECCV16(V: 437-453).
Springer DOI
1611
BibRef
van Gemert, J.C.[Jan C.],
Jain, M.[Mihir],
Gati, E.[Ella],
Snoek, C.G.M.[Cees G.M.],
APT: Action localization proposals from dense trajectories,
BMVC15(xx-yy).
DOI Link
1601
BibRef
Jain, M.[Mihir],
van Gemert, J.C.[Jan C.],
Mensink, T.[Thomas],
Snoek, C.G.M.[Cees G.M.],
Objects2action:
Classifying and Localizing Actions without Any Video Example,
ICCV15(4588-4596)
IEEE DOI
1602
Computational modeling
BibRef
Soomro, K.[Khurram],
Idrees, H.[Haroon],
Shah, M.[Mubarak],
Online Localization and Prediction of Actions and Interactions,
PAMI(41), No. 2, February 2019, pp. 459-472.
IEEE DOI
1901
BibRef
Earlier:
Predicting the Where and What of Actors and Actions through Online
Action Localization,
CVPR16(2648-2657)
IEEE DOI
1612
BibRef
Earlier:
Action Localization in Videos through Context Walk,
ICCV15(3280-3288)
IEEE DOI
1602
Videos, Support vector machines, Predictive models,
Motion segmentation, Visualization, Training, Dynamic programming,
structural SVM.
Context
BibRef
Soomro, K.[Khurram],
Shah, M.[Mubarak],
Unsupervised Action Discovery and Localization in Videos,
ICCV17(696-705)
IEEE DOI
1802
directed graphs, feature extraction, image classification,
image segmentation, knapsack problems, pattern clustering,
Videos
BibRef
Song, H.,
Wu, X.,
Zhu, B.,
Wu, Y.,
Chen, M.,
Jia, Y.,
Temporal Action Localization in Untrimmed Videos Using Action Pattern
Trees,
MultMed(21), No. 3, March 2019, pp. 717-730.
IEEE DOI
1903
data mining, feature extraction, image motion analysis,
image segmentation, learning (artificial intelligence),
overlap loss function
BibRef
Zeng, R.H.[Run-Hao],
Gan, C.[Chuang],
Chen, P.H.[Pei-Hao],
Huang, W.B.[Wen-Bing],
Wu, Q.Y.[Qing-Yao],
Tan, M.K.[Ming-Kui],
Breaking Winner-Takes-All: Iterative-Winners-Out Networks for Weakly
Supervised Temporal Action Localization,
IP(28), No. 12, December 2019, pp. 5797-5808.
IEEE DOI
1909
Videos, Training, Proposals, Image segmentation, Correlation,
Object detection, Semantics, Weakly supervised learning,
untrimmed video
BibRef
Zhang, Y.Q.[Yong-Qiang],
Ding, M.L.[Ming-Li],
Bai, Y.C.[Yan-Cheng],
Liu, D.[Dandan],
Ghanem, B.[Bernard],
Learning a strong detector for action localization in videos,
PRL(128), 2019, pp. 407-413.
Elsevier DOI
1912
Frame-level object detection, Deformable anchor cuboid, Action localization
BibRef
Heilbron, F.C.[Fabian Caba],
Lee, J.Y.[Joon-Young],
Jin, H.L.[Hai-Lin],
Ghanem, B.[Bernard],
What Do I Annotate Next? An Empirical Study of Active Learning for
Action Localization,
ECCV18(XI: 212-229).
Springer DOI
1810
BibRef
Heilbron, F.C.[Fabian Caba],
Barrios, W.,
Escorcia, V.,
Ghanem, B.[Bernard],
SCC: Semantic Context Cascade for Efficient Action Detection,
CVPR17(3175-3184)
IEEE DOI
1711
Computational modeling, Context modeling, Dogs, Legged locomotion,
Proposals, Semantics, Video, sequences
BibRef
Escorcia, V.[Victor],
Heilbron, F.C.[Fabian Caba],
Niebles, J.C.[Juan Carlos],
Ghanem, B.[Bernard],
DAPs: Deep Action Proposals for Action Understanding,
ECCV16(III: 768-784).
Springer DOI
1611
BibRef
Heilbron, F.C.[Fabian Caba],
Thabet, A.[Ali],
Niebles, J.C.[Juan Carlos],
Ghanem, B.[Bernard],
Camera Motion and Surrounding Scene Appearance as Context for Action
Recognition,
ACCV14(IV: 583-597).
Springer DOI
1504
BibRef
Long, F.C.[Fu-Chen],
Yao, T.[Ting],
Qiu, Z.F.[Zhao-Fan],
Tian, X.M.[Xin-Mei],
Mei, T.[Tao],
Luo, J.B.[Jie-Bo],
Coarse-to-Fine Localization of Temporal Action Proposals,
MultMed(22), No. 6, June 2020, pp. 1577-1590.
IEEE DOI
2005
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
Gaussian Temporal Awareness Networks for Action Localization,
CVPR19(344-353).
IEEE DOI
2002
Proposals, Videos, Painting, Brushes, Task analysis,
Feature extraction, Action Proposals, Action Recognition,
Video Captioning
BibRef
Kumar, N.,
Sukavanam, N.,
Weakly supervised deep network for spatiotemporal localization and
detection of human actions in wild conditions,
VC(36), No. 9, September 2020, pp. 1809-1821.
Springer DOI
2008
BibRef
Yang, L.,
Peng, H.,
Zhang, D.,
Fu, J.,
Han, J.,
Revisiting Anchor Mechanisms for Temporal Action Localization,
IP(29), 2020, pp. 8535-8548.
IEEE DOI
2008
Temporal action localization, default anchor, anchor free, complementarity
BibRef
Xu, W.,
Yu, J.,
Miao, Z.,
Wan, L.,
Ji, Q.,
Spatio-Temporal Deep Q-Networks for Human Activity Localization,
CirSysVideo(30), No. 9, September 2020, pp. 2984-2999.
IEEE DOI
2009
Proposals, Reinforcement learning, Activity recognition,
Context modeling, Electron tubes,
seq-to-seq model
BibRef
Qin, X.,
Ge, Y.,
Yu, H.,
Chen, F.,
Yang, D.,
Spatial Enhancement and Temporal Constraint for Weakly Supervised
Action Localization,
SPLetters(27), 2020, pp. 1520-1524.
IEEE DOI
2009
Training, Proposals, Feature extraction,
Entropy, Signal processing, Signal processing algorithms,
confidence connectivity enhancement
BibRef
Li, Y.G.[Ye-Guang],
Zhang, M.Y.[Ming-Yuan],
Hu, L.[Liang],
Li, J.[Jun],
Wang, D.Q.[De-Qing],
Candidate region correlation for video action detection,
JVCIR(71), 2020, pp. 102818.
Elsevier DOI
2009
Deep learning, Action detection, Region correlation, Self-attention mechanism
BibRef
Chen, P.,
Gan, C.,
Shen, G.,
Huang, W.,
Zeng, R.,
Tan, M.,
Relation Attention for Temporal Action Localization,
MultMed(22), No. 10, October 2020, pp. 2723-2733.
IEEE DOI
2009
Proposals, Feature extraction, Task analysis, Object detection,
Deep learning, Sports, Semantics, Temporal action localization,
relation attention
BibRef
Zhang, S.,
Song, L.,
Gao, C.,
Sang, N.,
GLNet: Global Local Network for Weakly Supervised Action Localization,
MultMed(22), No. 10, October 2020, pp. 2610-2622.
IEEE DOI
2009
Annotations, Proposals, Predictive models, Task analysis,
Feature extraction, Electron tubes, Training,
weakly supervised
BibRef
Xu, L.[Liang],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
Feng, B.[Bin],
Cascaded Boundary Network for High-Quality Temporal Action Proposal
Generation,
CirSysVideo(30), No. 10, October 2020, pp. 3702-3713.
IEEE DOI
2010
Proposals, Videos, Feature extraction, Task analysis,
Object detection, Visualization, Correlation,
long short-term memory
BibRef
Li, J.,
Todorovic, S.,
Set-Constrained Viterbi for Set-Supervised Action Segmentation,
CVPR20(10817-10826)
IEEE DOI
2008
Training, Hidden Markov models, Viterbi algorithm, Neural networks,
Feature extraction, TV, Task analysis
BibRef
Huang, Y.,
Sugano, Y.,
Sato, Y.,
Improving Action Segmentation via Graph-Based Temporal Reasoning,
CVPR20(14021-14031)
IEEE DOI
2008
Task analysis, Convolution, Cognition, Predictive models,
Computer vision, Cameras, Glass
BibRef
Ramaswamy, A.,
Seemakurthy, K.,
Gubbi, J.,
Purushothaman, B.,
Spatio-temporal action detection and localization using a
hierarchical LSTM,
DeepVision20(3303-3312)
IEEE DOI
2008
Feature extraction, Computer architecture, Microprocessors,
Task analysis, Visualization, Proposals
BibRef
Gong, G.,
Wang, X.,
Mu, Y.,
Tian, Q.,
Learning Temporal Co-Attention Models for Unsupervised Video Action
Localization,
CVPR20(9816-9825)
IEEE DOI
2008
Training, Benchmark testing, Proposals, Task analysis,
Noise measurement, Convolution, TV
BibRef
Jain, M.,
Ghodrati, A.,
Snoek, C.G.M.,
ActionBytes: Learning From Trimmed Videos to Localize Actions,
CVPR20(1168-1177)
IEEE DOI
2008
Videos, Training, Feature extraction, Task analysis, Pipelines,
Testing, Semantics
BibRef
Zhang, D.,
Dai, X.,
Wang, Y.,
METAL: Minimum Effort Temporal Activity Localization in Untrimmed
Videos,
CVPR20(3881-3891)
IEEE DOI
2008
Videos, Training, Metals, Testing, Feature extraction,
Task analysis, Visualization
BibRef
Eun, H.J.[Hyun-Jun],
Moon, J.Y.[Jin-Young],
Park, J.Y.[Jong-Youl],
Jung, C.[Chanho],
Kim, C.[Changick],
Learning to Discriminate Information for Online Action Detection,
CVPR20(806-815)
IEEE DOI
2008
Logic gates, Streaming media, Task analysis, Feature extraction,
Benchmark testing, Telecommunications, Recurrent neural networks
BibRef
Shi, B.,
Dai, Q.,
Mu, Y.,
Wang, J.,
Weakly-Supervised Action Localization by Generative Attention
Modeling,
CVPR20(1006-1016)
IEEE DOI
2008
Feature extraction, Task analysis, Context modeling, Training,
Pipelines, Graphical models
BibRef
Aliakbarian, S.[Sadegh],
Saleh, F.S.[Fatemeh Sadat],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Gould, S.[Stephen],
A Stochastic Conditioning Scheme for Diverse Human Motion Prediction,
CVPR20(5222-5231)
IEEE DOI
2008
Perturbation methods, Stochastic processes, Decoding, Training,
Predictive models, Task analysis, Diversity reception
BibRef
Rodriguez-Opazo, C.[Cristian],
Marrese-Taylor, E.[Edison],
Saleh, F.S.[Fatemeh Sadat],
Li, H.[Hongdong],
Gould, S.[Stephen],
Proposal-free Temporal Moment Localization of a Natural-Language
Query in Video using Guided Attention,
WACV20(2453-2462)
IEEE DOI
2006
Proposals, Task analysis, Natural languages, Visualization,
Semantics, Robots
BibRef
Islam, A.,
Radke, R.J.,
Weakly Supervised Temporal Action Localization Using Deep Metric
Learning,
WACV20(536-545)
IEEE DOI
2006
Feature extraction, Measurement, Training, Task analysis,
Machine learning, Feeds, Face
BibRef
Rashid, M.,
Kjellström, H.,
Lee, Y.J.,
Action Graphs: Weakly-supervised Action Localization with Graph
Convolution Networks,
WACV20(604-613)
IEEE DOI
2006
Convolution, Training, Feature extraction, Testing,
Motion segmentation, Predictive models
BibRef
Miki, D.,
Chen, S.,
Demachi, K.,
Weakly Supervised Graph Convolutional Neural Network for Human Action
Localization,
WACV20(642-650)
IEEE DOI
2006
Feature extraction, Time series analysis, Training, Convolution,
Machine learning, Skeleton, Supervised learning
BibRef
Kwak, I.S.,
Guo, J.,
Hantman, A.,
Branson, K.,
Kriegman, D.,
Detecting the Starting Frame of Actions in Video,
WACV20(478-486)
IEEE DOI
2006
Mice, Neuroscience, Optimal matching, Neural activity,
Recurrent neural networks, Task analysis, Neurons
BibRef
Gleason, J.,
Schwarcz, S.,
Ranjan, R.,
Castillo, C.D.,
Chen, J.,
Chellappa, R.,
Activity Detection in Untrimmed Videos Using Chunk-based Classifiers,
WACVWS20(107-116)
IEEE DOI
2006
Videos, Task analysis, Proposals, Computer architecture,
Machine learning, Standards
BibRef
Gleason, J.,
Castillo, C.D.,
Chellappa, R.,
Real-time Detection of Activities in Untrimmed Videos,
WACVWS20(117-125)
IEEE DOI
2006
Videos, Proposals, Cameras, Real-time systems, Training,
Object detection, Measurement
BibRef
Rahman, M.A.,
Laganière, R.,
Single-Stage End-to-End Temporal Activity Detection in Untrimmed
Videos,
CRV20(206-213)
IEEE DOI
2006
temporal activity detection, activity recognition,
single-stage detection, 3D convolutional network
BibRef
Narayan, S.,
Cholakkal, H.,
Khan, F.S.,
Shao, L.,
3C-Net: Category Count and Center Loss for Weakly-Supervised Action
Localization,
ICCV19(8678-8686)
IEEE DOI
2004
Code, Counting.
WWW Link. feature extraction, image classification,
image sequences, video signal processing, Motion pictures
BibRef
Wu, W.,
He, D.,
Tan, X.,
Chen, S.,
Wen, S.,
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective
Untrimmed Video Recognition,
ICCV19(6221-6230)
IEEE DOI
2004
image classification, image motion analysis,
learning (artificial intelligence), Markov processes
BibRef
Gao, M.,
Xu, M.,
Davis, L.,
Socher, R.,
Xiong, C.,
StartNet: Online Detection of Action Start in Untrimmed Videos,
ICCV19(5541-5550)
IEEE DOI
2004
feature extraction, gesture recognition,
image classification, image colour analysis,
Training data
BibRef
Wehrmann, J.,
Lopes, M.A.,
Souza, D.,
Barros, R.,
Language-Agnostic Visual-Semantic Embeddings,
ICCV19(5803-5812)
IEEE DOI
2004
Code, Visualization.
WWW Link. data visualisation, information retrieval,
learning (artificial intelligence),
Architecture
BibRef
Zeng, R.,
Huang, W.,
Gan, C.,
Tan, M.,
Rong, Y.,
Zhao, P.,
Huang, J.,
Graph Convolutional Networks for Temporal Action Localization,
ICCV19(7093-7102)
IEEE DOI
2004
convolutional neural nets, graph theory,
image classification, learning (artificial intelligence),
action proposal graph
BibRef
Pramono, R.R.A.,
Chen, Y.,
Fang, W.,
Hierarchical Self-Attention Network for Action Localization in Videos,
ICCV19(61-70)
IEEE DOI
2004
cameras, clutter, convolutional neural nets, image capture,
image fusion, image motion analysis, image recognition, Training
BibRef
Nguyen, P.,
Ramanan, D.,
Fowlkes, C.,
Weakly-Supervised Action Localization With Background Modeling,
ICCV19(5501-5510)
IEEE DOI
2004
image motion analysis, image sequences,
learning (artificial intelligence), multimedia Web sites, Training data
BibRef
Zhai, C.B.[Chang-Bo],
Wang, L.[Le],
Zhang, Q.L.[Qi-Lin],
Gao, Z.N.[Zhan-Ning],
Niu, Z.X.[Zhen-Xing],
Zheng, N.N.[Nan-Ning],
Hua, G.[Gang],
Action Co-localization in an Untrimmed Video by Graph Neural Networks,
MMMod20(I:555-567).
Springer DOI
2003
BibRef
Liu, D.C.[Dao-Chang],
Jiang, T.T.[Ting-Ting],
Wang, Y.[Yizhou],
Completeness Modeling and Context Separation for Weakly Supervised
Temporal Action Localization,
CVPR19(1298-1307).
IEEE DOI
2002
BibRef
Su, R.[Rui],
Ouyang, W.L.[Wan-Li],
Zhou, L.P.[Lu-Ping],
Xu, D.[Dong],
Improving Action Localization by Progressive Cross-Stream Cooperation,
CVPR19(12008-12017).
IEEE DOI
2002
BibRef
Wang, W.N.[Wei-Ning],
Huang, Y.[Yan],
Wang, L.[Liang],
Language-Driven Temporal Activity Localization:
A Semantic Matching Reinforcement Learning Model,
CVPR19(334-343).
IEEE DOI
2002
BibRef
Li, H.,
Yang, J.,
Zhou, Y.,
Li, S.,
Rethinking Temporal Structure Modeling Method for Temporal Action
Localization,
ICIP19(3676-3680)
IEEE DOI
1910
Action localization, spatial-temporal feature,
video content analysis, supervised learning
BibRef
Nguyen, P.,
Han, B.,
Liu, T.,
Prasad, G.,
Weakly Supervised Action Localization by Sparse Temporal Pooling
Network,
CVPR18(6752-6761)
IEEE DOI
1812
Videos, Proposals, Feature extraction, Task analysis,
Convolutional neural networks, Prediction algorithms
BibRef
Vial, R.,
Zhu, H.,
Tian, Y.,
Lu, S.,
Search video action proposal with recurrent and static YOLO,
ICIP17(2035-2039)
IEEE DOI
1803
Clutter, Detectors, Dynamic programming, Labeling, Object detection,
Proposals, Training, action detection, action localization,
video object proposal
BibRef
Shao, D.[Dian],
Xiong, Y.[Yu],
Zhao, Y.[Yue],
Huang, Q.Q.[Qing-Qiu],
Qiao, Y.[Yu],
Lin, D.[Dahua],
Find and Focus: Retrieve and Localize Video Events with Natural
Language Queries,
ECCV18(IX: 202-218).
Springer DOI
1810
BibRef
Sharir, G.[Gilad],
Tuytelaars, T.[Tinne],
Action in chains:
A chains model for action localization and classification,
WACV14(610-617)
IEEE DOI
1406
Computational modeling
BibRef
Ta, A.P.[Anh-Phuong],
Wolf, C.[Christian],
Lavoue, G.[Guillaume],
Baskurt, A.[Atilla],
Jolion, J.M.[Jean-Michel],
Pairwise Features for Human Action Recognition,
ICPR10(3224-3227).
IEEE DOI
1008
BibRef
And: A1, A2, A3, A4, Only:
Recognizing and Localizing Individual Activities through Graph Matching,
AVSS10(196-203).
IEEE DOI
1009
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Accumulation Methods, Motion Histograms for Human Action Recognition .