19.4.5.6.1 Video Understanding

Chapter Contents (Back)
Video Understanding.

Brostow, G.J.[Gabriel J.], Fauqueur, J.[Julien], Cipolla, R.[Roberto],
Semantic object classes in video: A high-definition ground truth database,
PRL(30), No. 2, 15 January 2009, pp. 88-97.
Elsevier DOI 0804
Object recognition; Video database; Video understanding; Semantic segmentation; Label propagation BibRef

Aodha, O.M.[Oisin Mac], Brostow, G.J.[Gabriel J.], Pollefeys, M.[Marc],
Segmenting video into classes of algorithm-suitability,
CVPR10(1054-1061).
IEEE DOI 1006
BibRef


Kanehira, A.[Atsushi], Takemoto, K.[Kentaro], Inayoshi, S.[Sho], Harada, T.[Tatsuya],
Multimodal Explanations by Predicting Counterfactuality in Videos,
CVPR19(8586-8594).
IEEE DOI 2002
BibRef

Kanehira, A.[Atsushi], Harada, T.[Tatsuya],
Learning to Explain With Complemental Examples,
CVPR19(8595-8603).
IEEE DOI 2002
BibRef

Zhou, L.[Luowei], Kalantidis, Y.[Yannis], Chen, X.L.[Xin-Lei], Corso, J.J.[Jason J.], Rohrbach, M.[Marcus],
Grounded Video Description,
CVPR19(6571-6580).
IEEE DOI 2002
BibRef

Liu, X.Y.[Xing-Yu], Lee, J.Y.[Joon-Young], Jin, H.L.[Hai-Lin],
Learning Video Representations From Correspondence Proposals,
CVPR19(4268-4276).
IEEE DOI 2002
BibRef

Alayrac, J.B.[Jean-Baptiste], Carreira, J.[Joao], Zisserman, A.[Andrew],
The Visual Centrifuge: Model-Free Layered Video Representations,
CVPR19(2452-2461).
IEEE DOI 2002
BibRef

Xiong, B.[Bo], Kalantidis, Y.[Yannis], Ghadiyaram, D.[Deepti], Grauman, K.[Kristen],
Less Is More: Learning Highlight Detection From Video Duration,
CVPR19(1258-1267).
IEEE DOI 2002
BibRef

Zhang, D.[Da], 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 Iterative Graph Adjustment,
CVPR19(1247-1257).
IEEE DOI 2002
Key moments in scene. BibRef

Fan, L., Huang, W., Gan, C., Ermon, S., Gong, B., 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 BibRef

Huang, D., Ramanathan, V., Mahajan, D., Torresani, L., Paluri, M., Fei-Fei, L., 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 BibRef

Mahdisoltani, F.[Farzaneh], Memisevic, R.[Roland], Fleet, D.J.[David J.],
Hierarchical Video Understanding,
WiCV-E18(IV:659-663).
Springer DOI 1905
BibRef

Shin, K.S.[Kwang-Soo], Jeon, J.[Junhyeong], Lee, S.[Seungbin], Lim, B.[Boyoung], Jeong, M.S.[Min-Soo], Nang, J.[Jongho],
Approach for Video Classification with Multi-label on YouTube-8M Dataset,
Large-Scale18(IV:317-324).
Springer DOI 1905
BibRef

Skalic, M.[Miha], Austin, D.[David],
Building A Size Constrained Predictive Models for Video Classification,
Large-Scale18(IV:297-305).
Springer DOI 1905
BibRef

Garg, S.[Shivam],
Learning Video Features for Multi-label Classification,
Large-Scale18(IV:325-337).
Springer DOI 1905
BibRef

Cho, C.[Choongyeun], Antin, B.[Benjamin], Arora, S.[Sanchit], Ashrafi, S.[Shwan], Duan, P.[Peilin], Huynh, D.T.[Dang The], James, L.[Lee], Nguyen, H.T.[Hang Tuan], Solgi, M.[Mojtaba], Than, C.V.[Cuong Van],
Large-Scale Video Classification with Feature Space Augmentation Coupled with Learned Label Relations and Ensembling,
Large-Scale18(IV:338-346).
Springer DOI 1905
BibRef

Lin, R.C.[Rong-Cheng], Xiao, J.[Jing], Fan, J.P.[Jian-Ping],
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-Level Features for Large-Scale Video Classification,
Large-Scale18(IV:206-218).
Springer DOI 1905
BibRef

Tang, Y.Y.[Yong-Yi], Zhang, X.[Xing], Wang, J.W.[Jing-Wen], Chen, S.X.[Shao-Xiang], Ma, L.[Lin], Jiang, Y.G.[Yu-Gang],
Non-local NetVLAD Encoding for Video Classification,
Large-Scale18(IV:219-228).
Springer DOI 1905
BibRef

Kmiec, S.[Sebastian], Bae, J.[Juhan], An, R.[Ruijian],
Learnable Pooling Methods for Video Classification,
Large-Scale18(IV:229-238).
Springer DOI 1905
BibRef

Liu, T.[Tianqi], Liu, B.[Bo],
Constrained-Size Tensorflow Models for YouTube-8M Video Understanding Challenge,
Large-Scale18(IV:239-249).
Springer DOI 1905
BibRef

Lee, J.[Joonseok], Natsev, A.P.[Apostol Paul], Reade, W.[Walter], Sukthankar, R.[Rahul], Toderici, G.[George],
The 2nd YouTube-8M Large-Scale Video Understanding Challenge,
Large-Scale18(IV:193-205).
Springer DOI 1905
BibRef

Zolfaghari, M.[Mohammadreza], Singh, K.[Kamaljeet], Brox, T.[Thomas],
ECO: Efficient Convolutional Network for Online Video Understanding,
ECCV18(II: 713-730).
Springer DOI 1810
BibRef

Sah, S., Nguyen, T., Dominguez, M., Such, F.P., Ptucha, R.,
Temporally Steered Gaussian Attention for Video Understanding,
DeepLearn-T17(2208-2216)
IEEE DOI 1709
Computational modeling, Decoding, Semantics, Standards, Streaming media, Training, Visualization BibRef

Jiang, Y.G.[Yu-Gang], Ye, G.[Guangnan], Chang, S.F.[Shih-Fu], Ellis, D.[Daniel], Loui, A.C.[Alexander C.],
Consumer video understanding: a benchmark database and an evaluation of human and machine performance,
ICMR11(29).
DOI Link 1301
BibRef

Yang, Y.[Yang], Liu, J.G.[Jin-Gen], Shah, M.[Mubarak],
Video Scene Understanding Using Multi-scale Analysis,
ICCV09(1669-1676).
IEEE DOI 0909
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Surveillance Video Summarization, Surveillance Synopsis .


Last update:Feb 20, 2020 at 21:34:09