Shi, Q.F.[Qin-Feng],
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Human Action Segmentation and Recognition Using Discriminative
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BibRef
Earlier: A1, A3, A2, A4:
Discriminative human action segmentation and recognition using
semi-Markov model,
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IEEE DOI
0806
BibRef
Wu, C.,
Zaheer, M.,
Hu, H.,
Manmatha, R.,
Smola, A.J.,
Krähenbühl, P.,
Compressed Video Action Recognition,
CVPR18(6026-6035)
IEEE DOI
1812
Image coding, Video compression, Training, Optical imaging,
Streaming media
BibRef
Samadani, A.A.[Ali-Akbar],
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Discriminative functional analysis of human movements,
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Elsevier DOI
1309
Human movement time-series analysis
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1605
Algorithm design and analysis
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Max-Margin Early Event Detectors,
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1404
BibRef
Earlier:
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IEEE DOI
1208
Award, CVPR, Student.
BibRef
Wang, Y.,
Hoai, M.,
Pulling Actions out of Context:
Explicit Separation for Effective Combination,
CVPR18(7044-7053)
IEEE DOI
1812
Training, Feature extraction, Context modeling, Cameras, Lighting,
Loss measurement, Video sequences
BibRef
Hoai, M.[Minh],
Lan, Z.Z.[Zhen-Zhong],
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Joint segmentation and classification of human actions in video,
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IEEE DOI
1106
BibRef
Wang, B.[Boyu],
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Back to the beginning:
Starting point detection for early recognition of ongoing human actions,
CVIU(175), 2018, pp. 24-31.
Elsevier DOI
1812
Action early recognition, Online action detection, Event detection
BibRef
Taralova, E.[Ekaterina],
de la Torre, F.[Fernando],
Hebert, M.[Martial],
Source constrained clustering,
ICCV11(1927-1934).
IEEE DOI
1201
Quantizing data from different sources. Cluster actions, not cluster
subjects.
BibRef
Panagiotakis, C.[Costas],
Papoutsakis, K.E.[Konstantinos E.],
Argyros, A.A.[Antonis A.],
A graph-based approach for detecting common actions in motion capture
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PR(79), 2018, pp. 1-11.
Elsevier DOI
1804
Common action detection, Video co-segmentation,
Temporal action co-segmentation, Dynamic Time Warping
BibRef
Zeng, X.X.[Xun-Xun],
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Wang, M.Q.[Mei-Qing],
Shape group Boltzmann machine for simultaneous object segmentation
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PRL(111), 2018, pp. 43-50.
Elsevier DOI
1808
Deep Boltzmann machine, Shape prior, Object segmentation,
Classification, Transformation invariance
BibRef
Duan, B.[Bin],
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Zhu, Y.[Ye],
Yan, Y.[Yan],
Mining and Unifying Heterogeneous Contrastive Relations for
Weakly-Supervised Actor-Action Segmentation,
WACV24(483-492)
IEEE DOI
2404
Training, Visualization, Costs, Motion segmentation, Semantics,
Pipelines, Algorithms, Image recognition and understanding,
Video recognition and understanding
BibRef
Yan, Y.[Yan],
Xu, C.L.[Chen-Liang],
Cai, D.[Dawen],
Corso, J.J.[Jason J.],
A Weakly Supervised Multi-task Ranking Framework for Actor-Action
Semantic Segmentation,
IJCV(128), No. 5, May 2020, pp. 1414-1432.
Springer DOI
2005
BibRef
Earlier:
Weakly Supervised Actor-Action Segmentation via Robust Multi-task
Ranking,
CVPR17(1022-1031)
IEEE DOI
1711
Optimization, Robustness, Semantics, Support vector machines,
Training, Videos
BibRef
Xu, C.L.[Chen-Liang],
Hsieh, S.H.[Shao-Hang],
Xiong, C.M.[Cai-Ming],
Corso, J.J.[Jason J.],
Can humans fly? Action understanding with multiple classes of actors,
CVPR15(2264-2273)
IEEE DOI
1510
BibRef
Chen, J.[Jie],
Li, Z.H.[Zhi-Heng],
Luo, J.B.[Jie-Bo],
Xu, C.L.[Chen-Liang],
Learning a Weakly-Supervised Video Actor-Action Segmentation Model
With a Wise Selection,
CVPR20(9898-9908)
IEEE DOI
2008
Training, Motion segmentation, Legged locomotion, Task analysis,
Computational modeling, Proposals
BibRef
Xu, C.L.[Chen-Liang],
Ding, L.[Li],
Weakly-Supervised Action Segmentation with Iterative Soft Boundary
Assignment,
CVPR18(6508-6516)
IEEE DOI
1812
Videos, Hidden Markov models, Training, Task analysis, Decoding,
Computational modeling, Recurrent neural networks
BibRef
Qian, H.W.[Hang-Wei],
Pan, S.J.L.[Sinno Jia-Lin],
Miao, C.Y.[Chun-Yan],
Weakly-supervised sensor-based activity segmentation and recognition
via learning from distributions,
AI(292), 2021, pp. 103429.
Elsevier DOI
2102
Human activity recognition, Sensor readings segmentation, Kernel mean embedding
BibRef
Sun, X.[Xiao],
Long, X.[Xiang],
He, D.L.[Dong-Liang],
Wen, S.L.[Shi-Lei],
Lian, Z.H.[Zhou-Hui],
VSRNet: End-to-end video segment retrieval with text query,
PR(119), 2021, pp. 108027.
Elsevier DOI
2106
Video segment retrieval, Video retrieval, Description localization
BibRef
Ji, L.[Lei],
Wu, C.[Chenfei],
Zhou, D.[Daisy],
Yan, K.[Kun],
Cui, E.[Edward],
Chen, X.L.[Xi-Lin],
Duan, N.[Nan],
Learning Temporal Video Procedure Segmentation from an Automatically
Collected Large Dataset,
WACV22(2733-2742)
IEEE DOI
2202
Measurement, TV, Convolution, Annotations,
Computational modeling, Transformers, Datasets,
Evaluation and Comparison of Vision Algorithms Vision and Languages
BibRef
Park, J.Y.[Jun-Yong],
Kim, D.[Daekyum],
Huh, S.[Sejoon],
Jo, S.[Sungho],
Maximization and restoration: Action segmentation through dilation
passing and temporal reconstruction,
PR(129), 2022, pp. 108764.
Elsevier DOI
2206
Action segmentation, Temporal segmentation, Video understanding
BibRef
Gao, H.B.[Hong-Bo],
Lv, C.[Chen],
Zhang, T.[Tong],
Zhao, H.F.[Hong-Fei],
Jiang, L.[Lei],
Zhou, J.J.[Jun-Jie],
Liu, Y.C.[Yu-Chao],
Huang, Y.[Yi],
Han, C.[Chao],
A Structure Constraint Matrix Factorization Framework for Human
Behavior Segmentation,
Cyber(52), No. 12, December 2022, pp. 12978-12988.
IEEE DOI
2212
Clustering algorithms, Image segmentation, Principal component analysis,
Motion segmentation, Optimization, structure constraint
BibRef
Chen, Y.Z.[Yun-Ze],
Chen, M.J.[Meng-Juan],
Gu, Q.Y.[Qing-Yi],
Class-wise boundary regression by uncertainty in temporal action
detection,
IET-IPR(16), No. 14, 2022, pp. 3854-3862.
DOI Link
2212
BibRef
Aziere, N.[Nicolas],
Todorovic, S.[Sinisa],
Multistage temporal convolution transformer for action segmentation,
IVC(128), 2022, pp. 104567.
Elsevier DOI
2212
Action segmentation, Video understanding, Full supervision,
Transformer network, Hybrid models, CNNs
BibRef
Ding, G.D.[Guo-Dong],
Yao, A.[Angela],
Temporal Action Segmentation With High-Level Complex Activity Labels,
MultMed(25), 2023, pp. 1928-1939.
IEEE DOI
2306
Videos, Task analysis, Prototypes, Dairy products, Protocols,
Activity recognition, Powders, Temporal action segmentation
BibRef
Singhania, D.[Dipika],
Rahaman, R.[Rahul],
Yao, A.[Angela],
C2F-TCN: A Framework for Semi- and Fully-Supervised Temporal Action
Segmentation,
PAMI(45), No. 10, October 2023, pp. 11484-11501.
IEEE DOI
2310
BibRef
Ding, G.D.[Guo-Dong],
Yao, A.[Angela],
Leveraging Action Affinity and Continuity for Semi-supervised Temporal
Action Segmentation,
ECCV22(XXXV:17-32).
Springer DOI
2211
BibRef
Rahaman, R.[Rahul],
Singhania, D.[Dipika],
Thiery, A.[Alexandre],
Yao, A.[Angela],
A Generalized and Robust Framework for Timestamp Supervision in
Temporal Action Segmentation,
ECCV22(IV:279-296).
Springer DOI
2211
BibRef
Su, T.[Taiyi],
Wang, H.[Hanli],
Wang, L.[Lei],
Multi-Level Content-Aware Boundary Detection for Temporal Action
Proposal Generation,
IP(32), 2023, pp. 6090-6101.
IEEE DOI Code:
WWW Link.
2311
BibRef
Ding, G.D.[Guo-Dong],
Sener, F.[Fadime],
Yao, A.[Angela],
Temporal Action Segmentation: An Analysis of Modern Techniques,
PAMI(46), No. 2, February 2024, pp. 1011-1030.
IEEE DOI
2401
Point cloud compression, Geometry, Transforms, Encoding,
Transform coding, Dictionaries, Correlation
BibRef
Liu, S.[Siyu],
Cheng, J.[Jian],
Xia, Z.[Ziying],
Xi, Z.L.[Zhi-Long],
Hou, Q.[Qin],
Dong, Z.C.[Zhi-Cheng],
HCM: Online Action Detection With Hard Video Clip Mining,
MultMed(26), 2024, pp. 3626-3639.
IEEE DOI
2402
Measurement, Task analysis, Feature extraction, Compaction,
Streaming media, Optimization, Detectors, Online action detection,
intra-class feature compaction
BibRef
Ke, X.[Xiao],
Miao, X.[Xin],
Guo, W.Z.[Wen-Zhong],
U-Transformer-based multi-levels refinement for weakly supervised
action segmentation,
PR(149), 2024, pp. 110199.
Elsevier DOI
2403
Action segmentation, U-Transformer, Timestamp supervision,
Multi-stages refinement
BibRef
Zhang, L.[Libo],
Gu, X.[Xin],
Li, C.C.[Cong-Cong],
Luo, T.J.[Tie-Jian],
Fan, H.[Heng],
Local Compressed Video Stream Learning for Generic Event Boundary
Detection,
IJCV(132), No. 4, April 2024, pp. 1187-1204.
Springer DOI
2404
BibRef
Li, C.C.[Cong-Cong],
Wang, X.Y.[Xin-Yao],
Wen, L.Y.[Long-Yin],
Hong, D.X.[De-Xiang],
Luo, T.J.[Tie-Jian],
Zhang, L.[Libo],
End-to-End Compressed Video Representation Learning for Generic Event
Boundary Detection,
CVPR22(13947-13956)
IEEE DOI
2210
Representation learning, Training, Annotations, Shape,
Machine vision, Video sequences, Feature extraction,
Vision applications and systems
BibRef
Ma, Y.[Yuer],
Liu, Y.[Yi],
Wang, L.M.[Li-Min],
Kang, W.X.[Wen-Xiong],
Qiao, Y.[Yu],
Wang, Y.[Yali],
Dual Masked Modeling for Weakly-Supervised Temporal Boundary
Discovery,
MultMed(26), 2024, pp. 5694-5704.
IEEE DOI
2404
Proposals, Visualization, Grounding, Location awareness,
Feature extraction, Task analysis, Annotations, Temporal grounding,
weakly-supervised learning
BibRef
Goldbraikh, A.[Adam],
Shubi, O.[Omer],
Rubin, O.[Or],
Pugh, C.M.[Carla M.],
Laufer, S.[Shlomi],
MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for
action segmentation using sensor-augmented kinematics,
PR(156), 2024, pp. 110778.
Elsevier DOI Code:
WWW Link.
2408
Action segmentation, Kinematic data, Deep learning, Data augmentation
BibRef
Xing, Z.[Zheng],
Zhao, W.[Weibing],
Segmentation and Completion of Human Motion Sequence via Temporal
Learning of Subspace Variety Model,
IP(33), 2024, pp. 5783-5797.
IEEE DOI
2410
Motion segmentation, Feature extraction, Task analysis, Image segmentation,
Solid modeling, Mathematical models, subspace variety model
BibRef
Gedamu, K.[Kumie],
Ji, Y.L.[Yan-Li],
Yang, Y.[Yang],
Shao, J.[Jie],
Shen, H.T.[Heng Tao],
Self-Supervised Sub-Action Parsing Network for Semi-Supervised Action
Quality Assessment,
IP(33), 2024, pp. 6057-6070.
IEEE DOI
2411
Semantics, Semisupervised learning, Quality assessment,
Contrastive learning, Annotations, Training, Motion segmentation
BibRef
Moltisanti, D.[Davide],
Bilen, H.[Hakan],
Sevilla-Lara, L.[Laura],
Keller, F.[Frank],
Coarse or Fine? Recognising Action End States without Labels,
FGVC24(1191-1200)
IEEE DOI
2410
Training, Image segmentation, Adaptation models, Image recognition,
Focusing, Training data, Data models, adverb recognition,
object end-state recognition
BibRef
Xu, J.L.[Jing-Lin],
Yin, S.[Sibo],
Zhao, G.H.[Guo-Hao],
Wang, Z.[Zishuo],
Peng, Y.X.[Yu-Xin],
FineParser: A Fine-Grained Spatio-Temporal Action Parser for
Human-Centric Action Quality Assessment,
CVPR24(14628-14637)
IEEE DOI Code:
WWW Link.
2410
Codes, Annotations, Focusing, Quality assessment,
Action Quality Assessment,
Human-centric Foreground Action Masks
BibRef
Chen, Z.W.[Zhi-Wen],
Zhu, Z.Y.[Zhi-Yu],
Zhang, Y.F.[Yi-Fan],
Hou, J.H.[Jun-Hui],
Shi, G.M.[Guang-Ming],
Wu, J.J.[Jin-Jian],
Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens,
CVPR24(3890-3900)
IEEE DOI Code:
WWW Link.
2410
Heart, Image segmentation, Object segmentation,
Computer architecture, Robustness, Data models, segmentation,
event-based vision
BibRef
Ding, G.D.[Guo-Dong],
Golong, H.[Hans],
Yao, A.[Angela],
Coherent Temporal Synthesis for Incremental Action Segmentation,
CVPR24(28485-28494)
IEEE DOI
2410
Adaptation models, Incremental learning, Computational modeling,
Focusing, Coherence, Benchmark testing
BibRef
Xu, A.[Angchi],
Zheng, W.S.[Wei-Shi],
Efficient and Effective Weakly-Supervised Action Segmentation via
Action-Transition-Aware Boundary Alignment,
CVPR24(18253-18262)
IEEE DOI Code:
WWW Link.
2410
Training, Visualization, Semantics, Noise, Robustness
BibRef
Shen, Y.H.[Yu-Han],
Elhamifar, E.[Ehsan],
Progress-Aware Online Action Segmentation for Egocentric Procedural
Task Videos,
CVPR24(18186-18197)
IEEE DOI Code:
WWW Link.
2410
Training, Uncertainty, Computational modeling,
Computer architecture, Videos
BibRef
Lu, Z.[Zijia],
Elhamifar, E.[Ehsan],
FACT: Frame-Action Cross-Attention Temporal Modeling for Efficient
Action Segmentation,
CVPR24(18175-18185)
IEEE DOI
2410
Computational modeling, Semantics, Computer architecture,
Parallel processing, Transformers,
Action Alignment
BibRef
\
Hirsch, R.[Roy],
Cohen, R.[Regev],
Golany, T.[Tomer],
Freedman, D.[Daniel],
Rivlin, E.[Ehud],
Random Walks for Temporal Action Segmentation with Timestamp
Supervision,
WACV24(6600-6610)
IEEE DOI
2404
Training, Adaptation models, Uncertainty, Smoothing methods,
Image annotation, Predictive models, Algorithms
BibRef
Tran, Q.H.[Quoc-Huy],
Mehmood, A.[Ahmed],
Ahmed, M.[Muhammad],
Naufil, M.[Muhammad],
Zafar, A.[Anas],
Konin, A.[Andrey],
Zia, M.Z.[M. Zeeshan],
Permutation-Aware Activity Segmentation via Unsupervised
Frame-to-Segment Alignment,
WACV24(6412-6422)
IEEE DOI
2404
Training, Video on demand, Predictive models, Transformers, Decoding,
Web sites, Algorithms, Video recognition and understanding
BibRef
Li, Y.R.[Yue-Rong],
Xue, Z.R.[Zheng-Rong],
Xu, H.Z.[Hua-Zhe],
OTAS: Unsupervised Boundary Detection for Object-Centric Temporal
Action Segmentation,
WACV24(6423-6432)
IEEE DOI
2404
Measurement, Visualization, Fuses, Annotations, Feature extraction,
Real-time systems, Algorithms, Video recognition and understanding
BibRef
Bahrami, E.[Emad],
Francesca, G.[Gianpiero],
Gall, J.[Juergen],
How Much Temporal Long-Term Context is Needed for Action
Segmentation?,
ICCV23(10317-10327)
IEEE DOI
2401
BibRef
Ma, K.[Kaijing],
Zang, X.[Xianghao],
Feng, Z.[Zerun],
Fang, H.[Han],
Ban, C.[Chao],
Wei, Y.H.[Yu-Han],
He, Z.J.[Zhong-Jiang],
Li, Y.X.[Yong-Xiang],
Sun, H.[Hao],
LLaViLo: Boosting Video Moment Retrieval via Adapter-Based Multimodal
Modeling,
CLVL23(2790-2795)
IEEE DOI
2401
BibRef
Liu, D.C.[Dao-Chang],
Li, Q.Y.[Qi-Yue],
Dinh, A.D.[Anh-Dung],
Jiang, T.T.[Ting-Ting],
Shah, M.[Mubarak],
Xu, C.[Chang],
Diffusion Action Segmentation,
ICCV23(10105-10115)
IEEE DOI
2401
BibRef
Aziere, N.[Nicolas],
Todorovic, S.[Sinisa],
Markov Game Video Augmentation for Action Segmentation,
ICCV23(13459-13468)
IEEE DOI
2401
BibRef
Jiang, B.[Borui],
Jin, Y.[Yang],
Tan, Z.T.[Zhen-Tao],
Mu, Y.D.[Ya-Dong],
Video Action Segmentation via Contextually Refined Temporal Keypoints,
ICCV23(13790-13799)
IEEE DOI
2401
BibRef
Liu, K.Y.[Kai-Yuan],
Li, Y.H.[Yun-Heng],
Liu, S.L.[Sheng-Lan],
Tan, C.W.[Chen-Wei],
Shao, Z.H.[Zi-Hang],
Reducing the Label Bias for Timestamp Supervised Temporal Action
Segmentation,
CVPR23(6503-6513)
IEEE DOI
2309
BibRef
van Amsterdam, B.[Beatrice],
Kadkhodamohammadi, A.[Abdolrahim],
Luengo, I.[Imanol],
Stoyanov, D.[Danail],
ASPnet: Action Segmentation with Shared-Private Representation of
Multiple Data Sources,
CVPR23(2384-2393)
IEEE DOI
2309
BibRef
Han, H.F.[Hong-Feng],
Lu, Z.W.[Zhi-Wu],
Wen, J.R.[Ji-Rong],
CTDA: Contrastive Temporal Domain Adaptation for Action Segmentation,
MMMod23(II: 562-574).
Springer DOI
2304
BibRef
Behrmann, N.[Nadine],
Golestaneh, S.A.[S. Alireza],
Kolter, Z.[Zico],
Gall, J.[Jürgen],
Noroozi, M.[Mehdi],
Unified Fully and Timestamp Supervised Temporal Action Segmentation via
Sequence to Sequence Translation,
ECCV22(XXXV:52-68).
Springer DOI
2211
BibRef
Ishihara, K.[Kenta],
Nakano, G.[Gaku],
Inoshita, T.[Tetsuo],
MCFM: Mutual Cross Fusion Module for Intermediate Fusion-Based Action
Segmentation,
ICIP22(1701-1705)
IEEE DOI
2211
Measurement, Image segmentation, Action segmentation,
feature fusion, mutual cross fusion module, human-related feature
BibRef
Sun, Z.N.[Zhao-Ning],
Messikommer, N.[Nico],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
ESS: Learning Event-Based Semantic Segmentation from Still Images,
ECCV22(XXXIV:341-357).
Springer DOI
2211
BibRef
Chen, L.[Lei],
Tong, Z.[Zhan],
Song, Y.B.[Yi-Bing],
Wu, G.S.[Gang-Shan],
Wang, L.M.[Li-Min],
Efficient Video Action Detection with Token Dropout and Context
Refinement,
ICCV23(10354-10365)
IEEE DOI Code:
WWW Link.
2401
BibRef
Tang, J.Q.[Jia-Qi],
Liu, Z.Y.[Zhao-Yang],
Qian, C.[Chen],
Wu, W.[Wayne],
Wang, L.M.[Li-Min],
Progressive Attention on Multi-Level Dense Difference Maps for
Generic Event Boundary Detection,
CVPR22(3345-3354)
IEEE DOI
2210
Representation learning, Codes, Aggregates, Semantics,
Benchmark testing,
Action and event recognition
BibRef
Du, Z.X.[Ze-Xing],
Wang, X.[Xue],
Zhou, G.Q.[Guo-Qing],
Wang, Q.[Qing],
Fast and Unsupervised Action Boundary Detection for Action
Segmentation,
CVPR22(3313-3322)
IEEE DOI
2210
Training, Clustering algorithms, Real-time systems,
Proposals, Task analysis,
Action and event recognition
BibRef
Kang, H.[Hyolim],
Kim, J.[Jinwoo],
Kim, T.[Taehyun],
Kim, S.J.[Seon Joo],
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event
Boundary Detection,
CVPR22(20041-20050)
IEEE DOI
2210
Computational modeling, Semantics, Benchmark testing,
Task analysis, Action and event recognition,
Video analysis and understanding
BibRef
Kumar, S.[Sateesh],
Haresh, S.[Sanjay],
Ahmed, A.[Awais],
Konin, A.[Andrey],
Zia, M.Z.[M. Zeeshan],
Tran, Q.H.[Quoc-Huy],
Unsupervised Action Segmentation by Joint Representation Learning and
Online Clustering,
CVPR22(20142-20153)
IEEE DOI
2210
Representation learning, Visualization, Video on demand,
Memory management, Task analysis,
Self- semi- meta- Video analysis and understanding
BibRef
Dimiccoli, M.[Mariella],
Garrido, L.[Lluís],
Rodriguez-Corominas, G.[Guillem],
Wendt, H.[Herwig],
Graph Constrained Data Representation Learning for Human Motion
Segmentation,
ICCV21(1440-1449)
IEEE DOI
2203
Analytical models, Dictionaries, Computational modeling,
Motion segmentation, Transfer learning, Benchmark testing,
grouping and shape
BibRef
Ahn, H.[Hyemin],
Lee, D.[Dongheui],
Refining Action Segmentation with Hierarchical Video Representations,
ICCV21(16282-16290)
IEEE DOI
2203
Training, Codes, Computational modeling, Refining, Predictive models,
Feature extraction, Action and behavior recognition,
Video analysis and understanding
BibRef
Lu, Z.J.[Zi-Jia],
Elhamifar, E.[Ehsan],
Set-Supervised Action Learning in Procedural Task Videos via Pairwise
Order Consistency,
CVPR22(19871-19881)
IEEE DOI
2210
Training, Location awareness, Shape,
Reliability, Task analysis, Action and event recognition,
Video analysis and understanding
BibRef
Lu, Z.J.[Zi-Jia],
Elhamifar, E.[Ehsan],
Weakly-Supervised Action Segmentation and Alignment via
Transcript-Aware Union-of-Subspaces Learning,
ICCV21(8065-8075)
IEEE DOI
2203
Training, Real-time systems,
Inference algorithms, Videos, Video analysis and understanding,
grouping and shape
BibRef
Li, J.[Jun],
Todorovic, S.[Sinisa],
Action Shuffle Alternating Learning for Unsupervised Action
Segmentation,
CVPR21(12623-12631)
IEEE DOI
2111
Training, Viterbi algorithm,
Computational modeling, Hidden Markov models,
Videos
BibRef
Shen, Y.H.[Yu-Han],
Wang, L.[Lu],
Elhamifar, E.[Ehsan],
Learning to Segment Actions from Visual and Language Instructions via
Differentiable Weak Sequence Alignment,
CVPR21(10151-10160)
IEEE DOI
2111
Location awareness, Visualization,
Computational modeling, Prototypes, Linguistics, Feature extraction
BibRef
Ishikawa, Y.[Yuchi],
Kasai, S.[Seito],
Aoki, Y.[Yoshimitsu],
Kataoka, H.[Hirokatsu],
Alleviating Over-segmentation Errors by Detecting Action Boundaries,
WACV21(2321-2330)
IEEE DOI
2106
Segmenting actions.
Smoothing methods, Refining, Feature extraction, Task analysis
BibRef
Nicora, E.[Elena],
Pastore, V.P.[Vito Paolo],
Noceti, N.[Nicoletta],
GCK-Maps: A Scene Unbiased Representation for Efficient Human Action
Recognition,
CIAP23(I:62-73).
Springer DOI
2312
BibRef
Vignolo, A.[Alessia],
Noceti, N.[Nicoletta],
Sciutti, A.[Alessandra],
Odone, F.[Francesca],
Sandini, G.[Giulio],
Learning dictionaries of kinematic primitives for action
classification,
ICPR21(5965-5972)
IEEE DOI
2105
Visualization, Dictionaries, Motion segmentation, Kinematics,
Encoding, Synchronization
BibRef
Li, J.[Jun],
Todorovic, S.[Sinisa],
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation,
CVPR21(9801-9810)
IEEE DOI
2111
BibRef
Earlier:
Set-Constrained Viterbi for Set-Supervised Action Segmentation,
CVPR20(10817-10826)
IEEE DOI
2008
Training, Shortest path problem, Monte Carlo methods,
Viterbi algorithm, Hidden Markov models, Estimation, Benchmark testing.
Neural networks,
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ICPR18(2534-2539)
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1812
Videos, Feature extraction, Motion segmentation,
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ICIP18(1892-1896)
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Videos, Motion segmentation, Training, Indexes, Hidden Markov models,
Clustering algorithms, Shape, community detection,
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WACV16(1-8)
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Temporal Subspace Clustering for Human Motion Segmentation,
ICCV15(4453-4461)
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CVPR15(3762-3771)
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1510
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Kim, Y.[Yelin],
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Modeling transition patterns between events for temporal human action
segmentation and classification,
FG15(1-8)
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dynamic programming
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WACV14(618-625)
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Accumulation Methods, Motion Histograms for Human Action Recognition .