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Elsevier DOI
0804
Object recognition; Video database; Video understanding; Semantic
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BibRef
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Pollefeys, M.[Marc],
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Video description, Feature enhancing, CNN, LSTM, Semantic
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Learning Dual Encoding Model for Adaptive Visual Understanding in
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IP(30), 2021, pp. 220-233.
IEEE DOI
2011
Visualization, Semantics, History, Task analysis, Cognition,
Feature extraction, Adaptation models, Dual encoding,
visual dialogue
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Video representation learning,
Long-range dependencies capturing, Video classification
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Elsevier DOI
2012
Video datasets, depicting series of actions performed in some
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Zhao, Y.[Yue],
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Omni-sourced Webly-supervised Learning for Video Recognition,
ECCV20(XV:670-688).
Springer DOI
2011
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Kumar, A.,
Pande, S.,
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Chaudhuri, S.,
MT-UNET: A Novel U-Net Based Multi-Task Architecture For Visual Scene
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ICIP20(2191-2195)
IEEE DOI
2011
Task analysis, Decoding, Feature extraction, Semantics,
Loss measurement, Image segmentation, Estimation,
deep learning
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Fayyaz, M.[Mohsen],
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Paluri, M.[Manohar],
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Large Scale Holistic Video Understanding,
ECCV20(V:593-610).
Springer DOI
2011
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Pont-Tuset, J.[Jordi],
Uijlings, J.[Jasper],
Changpinyo, S.[Soravit],
Soricut, R.[Radu],
Ferrari, V.[Vittorio],
Connecting Vision and Language with Localized Narratives,
ECCV20(V:647-664).
Springer DOI
2011
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Cotter, F.[Fergal],
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Probabilistic Future Prediction for Video Scene Understanding,
ECCV20(XVI: 767-785).
Springer DOI
2010
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Haro, B.B.[Benjamín Béjar],
Vidal, R.[René],
Representation Learning on Visual-Symbolic Graphs for Video
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ECCV20(XXIX: 71-90).
Springer DOI
2010
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Singhania, D.[Dipika],
Yao, A.[Angela],
Temporal Aggregate Representations for Long-range Video Understanding,
ECCV20(XVI: 154-171).
Springer DOI
2010
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Cho, M.[Minsu],
Motionsqueeze: Neural Motion Feature Learning for Video Understanding,
ECCV20(XVI: 345-362).
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2010
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Salti, S.,
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Mattoccia, S.,
Distilled Semantics for Comprehensive Scene Understanding from Videos,
CVPR20(4653-4664)
IEEE DOI
2008
Semantics, Optical imaging, Cameras, Videos, Training, Estimation,
Computer vision
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Angelova, A.[Anelia],
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Evolving Losses for Unsupervised Video Representation Learning,
CVPR20(130-139)
IEEE DOI
2008
Task analysis, Optical losses, Labeling, Training,
Evolutionary computation, Kinetic theory, Loss measurement
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Huang, Q.,
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A Graph-Based Framework to Bridge Movies and Synopses,
ICCV19(4591-4600)
IEEE DOI
2004
Code, Video Understanding.
WWW Link. entertainment, graph theory, video signal processing,
graph-based framework, video analytics, movie understanding,
Computer vision
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TSM: Temporal Shift Module for Efficient Video Understanding,
ICCV19(7082-7092)
IEEE DOI
2004
Code, Video Understanding.
WWW Link. convolutional neural nets, object detection,
video signal processing, video streaming, Real-time systems
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Takemoto, K.[Kentaro],
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Multimodal Explanations by Predicting Counterfactuality in Videos,
CVPR19(8586-8594).
IEEE DOI
2002
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Learning to Explain With Complemental Examples,
CVPR19(8595-8603).
IEEE DOI
2002
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Kalantidis, Y.[Yannis],
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Rohrbach, M.[Marcus],
Grounded Video Description,
CVPR19(6571-6580).
IEEE DOI
2002
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Lee, J.Y.[Joon-Young],
Jin, H.L.[Hai-Lin],
Learning Video Representations From Correspondence Proposals,
CVPR19(4268-4276).
IEEE DOI
2002
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The Visual Centrifuge: Model-Free Layered Video Representations,
CVPR19(2452-2461).
IEEE DOI
2002
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Grauman, K.[Kristen],
Less Is More: Learning Highlight Detection From Video Duration,
CVPR19(1258-1267).
IEEE DOI
2002
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Dai, X.[Xiyang],
Wang, X.[Xin],
Wang, Y.F.[Yuan-Fang],
Davis, L.S.[Larry S.],
MAN: Moment Alignment Network for Natural Language Moment Retrieval via
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CVPR19(1247-1257).
IEEE DOI
2002
Key moments in scene.
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Fan, L.,
Huang, W.,
Gan, C.,
Ermon, S.,
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Huang, J.,
End-to-End Learning of Motion Representation for Video Understanding,
CVPR18(6016-6025)
IEEE DOI
1812
Optical imaging, Task analysis, Optical computing, Training,
Optical fiber networks, Brightness, Neural networks
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Huang, D.,
Ramanathan, V.,
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Torresani, L.,
Paluri, M.,
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Niebles, J.C.,
What Makes a Video a Video: Analyzing Temporal Information in Video
Understanding Models and Datasets,
CVPR18(7366-7375)
IEEE DOI
1812
Analytical models, Generators, Kinetic theory, Visualization,
Upper bound, Testing, Training
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Memisevic, R.[Roland],
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Hierarchical Video Understanding,
WiCV-E18(IV:659-663).
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1905
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Approach for Video Classification with Multi-label on YouTube-8M
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Large-Scale18(IV:317-324).
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1905
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Austin, D.[David],
Building A Size Constrained Predictive Models for Video Classification,
Large-Scale18(IV:297-305).
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1905
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Learning Video Features for Multi-label Classification,
Large-Scale18(IV:325-337).
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1905
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Large-Scale18(IV:338-346).
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1905
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NeXtVLAD: An Efficient Neural Network to Aggregate Frame-Level Features
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Large-Scale18(IV:206-218).
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Large-Scale18(IV:229-238).
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1905
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1905
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1810
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DeepLearn-T17(2208-2216)
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Computational modeling, Decoding, Semantics, Standards,
Streaming media, Training, Visualization
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Video Scene Understanding Using Multi-scale Analysis,
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IEEE DOI
0909
BibRef
Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Surveillance Video Summarization, Surveillance Synopsis .