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Cipolla, R.[Roberto],
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PRL(30), No. 2, 15 January 2009, pp. 88-97.
Elsevier DOI
0804
Object recognition; Video database; Video understanding; Semantic
segmentation; Label propagation
BibRef
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Brostow, G.J.[Gabriel J.],
Pollefeys, M.[Marc],
Segmenting video into classes of algorithm-suitability,
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IEEE DOI
1006
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MultInfoRetr(9), No. 2, June 2020, pp. 81-101.
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Springer DOI
2008
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Tan, B.[Bin],
Translating video into language by enhancing visual and language
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JVCIR(72), 2020, pp. 102875.
Elsevier DOI
2010
Video description, Feature enhancing, CNN, LSTM, Semantic
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Wu, Q.,
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
BibRef
Duan, J.H.[Jin-Hao],
Xu, H.[Hua],
Lin, X.Z.[Xiao-Zhu],
Zhu, S.C.[Shang-Chao],
Du, Y.Z.[Yuan-Ze],
Multi-semantic long-range dependencies capturing for efficient video
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Elsevier DOI
2012
Video representation learning,
Long-range dependencies capturing, Video classification
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Tan, H.L.[Hui Li],
Zhu, H.Y.[Hong-Yuan],
Lim, J.H.[Joo-Hwee],
Tan, C.[Cheston],
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CVIU(202), 2021, pp. 103107.
Elsevier DOI
2012
Video datasets, depicting series of actions performed in some
constrained but non-unique order to achieve some intended high-level
goal.
BibRef
Lin, J.[Ji],
Gan, C.[Chuang],
Wang, K.[Kuan],
Han, S.[Song],
TSM: Temporal Shift Module for Efficient and Scalable Video
Understanding on Edge Devices,
PAMI(44), No. 5, May 2022, pp. 2760-2774.
IEEE DOI
2204
BibRef
Earlier: A1, A2, A4, Only:
TSM: Temporal Shift Module for Efficient Video Understanding,
ICCV19(7082-7092)
IEEE DOI
2004
Code, Video Understanding.
WWW Link. Computational modeling, Convolution, Streaming media, Training,
Solid modeling, Temporal shift module, video recognition,
network dissection.
convolutional neural nets, object detection,
video signal processing, video streaming, Real-time systems
BibRef
Zhou, W.[Wei],
Hou, Y.[Yi],
Ouyang, K.W.[Ke-Wei],
Zhou, S.L.[Shi-Lin],
Exploring complementary information of self-supervised pretext tasks
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IET-CV(16), No. 3, 2022, pp. 255-265.
DOI Link
2204
Both knowledge distillation and self-supervised learning.
convolutional neural nets, feature extraction,
unsupervised learning, video signal processing, image sequences
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Li, Z.Q.[Zhen-Qiang],
Wang, W.M.[Wei-Min],
Li, Z.Y.[Zuo-Yue],
Huang, Y.F.[Yi-Fei],
Sato, Y.[Yoichi],
Spatio-Temporal Perturbations for Video Attribution,
CirSysVideo(32), No. 4, April 2022, pp. 2043-2056.
IEEE DOI
2204
Measurement, Reliability, Task analysis, Spatiotemporal phenomena,
Visualization, Heating systems, Perturbation methods, video understanding
BibRef
Tao, L.[Li],
Wang, X.T.[Xue-Ting],
Yamasaki, T.[Toshihiko],
An Improved Inter-Intra Contrastive Learning Framework on
Self-Supervised Video Representation,
CirSysVideo(32), No. 8, August 2022, pp. 5266-5280.
IEEE DOI
2208
Task analysis, Learning systems, Data models, Optical imaging,
Feature extraction, Representation learning, Optical sensors,
spatio-temporal convolution
BibRef
Huang, L.[Lang],
You, S.[Shan],
Zheng, M.K.[Ming-Kai],
Wang, F.[Fei],
Qian, C.[Chen],
Yamasaki, T.[Toshihiko],
Learning Where to Learn in Cross-View Self-Supervised Learning,
CVPR22(14431-14440)
IEEE DOI
2210
Representation learning, Image segmentation, Head, Aggregates,
Semantics, Self-supervised learning, Object detection,
Self- semi- meta- unsupervised learning
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Hu, Y.[Yaosi],
Yin, D.C.[Da-Cheng],
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Decomposing style, content, and motion for videos,
JVCIR(89), 2022, pp. 103686.
Elsevier DOI
2212
Video decomposition, Video synthesis, Self-supervised learning
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Hong, M.Y.[Ming-Yao],
Zhang, X.F.[Xin-Feng],
Li, G.R.[Guo-Rong],
Huang, Q.M.[Qing-Ming],
Fine-Grained Feature Generation for Generalized Zero-Shot Video
Classification,
IP(32), 2023, pp. 1599-1612.
IEEE DOI
2303
Visualization, Semantics, Task analysis, Training,
Generative adversarial networks, Feature extraction, Data models,
video classification
BibRef
Jin, X.[Xin],
Feng, R.[Ruoyu],
Sun, S.[Simeng],
Feng, R.[Runsen],
He, T.[Tianyu],
Chen, Z.B.[Zhi-Bo],
Semantical video coding: Instill static-dynamic clues into structured
bitstream for AI tasks,
JVCIR(93), 2023, pp. 103816.
Elsevier DOI
2305
Video coding, Semantically structured bitstream, Intelligent analytics
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Senocak, A.[Arda],
Kim, J.[Junsik],
Oh, T.H.[Tae-Hyun],
Li, D.Z.[Ding-Zeyu],
Kweon, I.S.[In So],
Event-Specific Audio-Visual Fusion Layers:
A Simple and New Perspective on Video Understanding,
WACV23(2236-2246)
IEEE DOI
2302
Benchmark testing, Multisensory integration, Floods, Task analysis,
Algorithms: Vision + language and/or other modalities
BibRef
Xia, B.Y.[Bo-Yang],
Wu, W.H.[Wen-Hao],
Wang, H.R.[Hao-Ran],
Su, R.[Rui],
He, D.L.[Dong-Liang],
Yang, H.[Haosen],
Fan, X.R.[Xiao-Ran],
Ouyang, W.L.[Wan-Li],
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition,
ECCV22(XXXIV:705-723).
Springer DOI
2211
BibRef
Xia, B.Y.[Bo-Yang],
Wang, Z.H.[Zhi-Hao],
Wu, W.H.[Wen-Hao],
Wang, H.R.[Hao-Ran],
Han, J.G.[Jun-Gong],
Temporal Saliency Query Network for Efficient Video Recognition,
ECCV22(XXXIV:741-759).
Springer DOI
2211
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Islam, M.M.[Md Mohaiminul],
Bertasius, G.[Gedas],
Long Movie Clip Classification with State-Space Video Models,
ECCV22(XXXV:87-104).
Springer DOI
2211
BibRef
Habibian, A.[Amirhossein],
Yahia, H.B.[Haitam Ben],
Abati, D.[Davide],
Gavves, E.[Efstratios],
Porikli, F.M.[Fatih M.],
Delta Distillation for Efficient Video Processing,
ECCV22(XXXV:213-229).
Springer DOI
2211
BibRef
Li, Z.Z.[Zi-Zhang],
Wang, M.M.[Meng-Meng],
Pi, H.J.[Huai-Jin],
Xu, K.[Kechun],
Mei, J.B.[Jian-Biao],
Liu, Y.[Yong],
E-NeRV: Expedite Neural Video Representation with Disentangled
Spatial-Temporal Context,
ECCV22(XXXV:267-284).
Springer DOI
2211
BibRef
Kosman, E.[Eitan],
di Castro, D.[Dotan],
GraphVid: It only Takes a Few Nodes to Understand a Video,
ECCV22(XXXV:195-212).
Springer DOI
2211
BibRef
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Han, T.[Tengda],
Zheng, K.[Kunhao],
Zhang, Y.[Ya],
Xie, W.[Weidi],
Prompting Visual-Language Models for Efficient Video Understanding,
ECCV22(XXXV:105-124).
Springer DOI
2211
BibRef
Liang, S.X.[Shu-Xian],
Shen, X.[Xu],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Delving into Details: Synopsis-to-Detail Networks for Video Recognition,
ECCV22(IV:262-278).
Springer DOI
2211
BibRef
Ur Rehman, Y.A.[Yasar Abbas],
Gao, Y.[Yan],
Shen, J.J.[Jia-Jun],
de Gusmão, P.P.B.[Pedro Porto Buarque],
Lane, N.[Nicholas],
Federated Self-supervised Learning for Video Understanding,
ECCV22(XXXI:506-522).
Springer DOI
2211
BibRef
Dadashzadeh, A.[Amirhossein],
Whone, A.[Alan],
Mirmehdi, M.[Majid],
Auxiliary Learning for Self-Supervised Video Representation via
Similarity-based Knowledge Distillation,
L3D-IVU22(4230-4239)
IEEE DOI
2210
Representation learning, Knowledge engineering, Training,
Predictive models, Data models, Pattern recognition, Reliability
BibRef
Li, Y.[Yi],
Vasconcelos, N.M.[Nuno M.],
Improving Video Model Transfer with Dynamic Representation Learning,
CVPR22(19258-19269)
IEEE DOI
2210
Representation learning, Knowledge engineering,
Analytical models, Computational modeling, Transfer learning,
Video analysis and understanding
BibRef
Guo, S.[Sheng],
Xiong, Z.[Zihua],
Zhong, Y.J.[Yu-Jie],
Wang, L.M.[Li-Min],
Guo, X.B.[Xiao-Bo],
Han, B.[Bing],
Huang, W.L.[Wei-Lin],
Cross-Architecture Self-supervised Video Representation Learning,
CVPR22(19248-19257)
IEEE DOI
2210
Representation learning, Video sequences,
Self-supervised learning, Predictive models, Video analysis and understanding
BibRef
Xu, X.Y.[Xin-Yu],
Li, Y.L.[Yong-Lu],
Lu, C.[Cewu],
Learning to Anticipate Future with Dynamic Context Removal,
CVPR22(12724-12734)
IEEE DOI
2210
WWW Link. Training, Visualization, Schedules, Uncertainty, Benchmark testing,
Transformers, Cognition, Visual reasoning, Video analysis and understanding
BibRef
Gadre, S.Y.[Samir Yitzhak],
Ehsani, K.[Kiana],
Song, S.[Shuran],
Mottaghi, R.[Roozbeh],
Continuous Scene Representations for Embodied AI,
CVPR22(14829-14839)
IEEE DOI
2210
Training, Representation learning, Visualization, Image analysis,
Navigation, Tracking, Robot vision systems, Robot vision,
Scene analysis and understanding
BibRef
Liang, C.[Chen],
Wang, W.G.[Wen-Guan],
Zhou, T.F.[Tian-Fei],
Yang, Y.[Yi],
Visual Abductive Reasoning,
CVPR22(15544-15554)
IEEE DOI
2210
Visualization, Reactive power, Transformers, Cognition,
Pattern recognition, Task analysis, Vision+language,
Video analysis and understanding
BibRef
Kinfu, K.A.[Kaleab A.],
Vidal, R.[René],
Analysis and Extensions of Adversarial Training for Video
Classification,
RoSe22(3415-3424)
IEEE DOI
2210
Training, Noise reduction,
Generative adversarial networks, Robustness, Pattern recognition
BibRef
Xiao, F.[Fanyi],
Kundu, K.[Kaustav],
Tighe, J.[Joseph],
Modolo, D.[Davide],
Hierarchical Self-supervised Representation Learning for Movie
Understanding,
CVPR22(9717-9726)
IEEE DOI
2210
Representation learning, Measurement, Soft sensors, Semantics,
Self-supervised learning, Benchmark testing, Motion pictures,
Video analysis and understanding
BibRef
Li, L.L.[Liu-Lei],
Zhou, T.F.[Tian-Fei],
Wang, W.G.[Wen-Guan],
Yang, L.[Lu],
Li, J.W.[Jian-Wu],
Yang, Y.[Yi],
Locality-Aware Inter-and Intra-Video Reconstruction for
Self-Supervised Correspondence Learning,
CVPR22(8709-8720)
IEEE DOI
2210
Representation learning, Location awareness, Visualization,
Semantics, Reconstruction algorithms, Encoding, grouping and shape analysis
BibRef
Jiang, Y.F.[Yi-Fan],
Gong, X.Y.[Xin-Yu],
Wu, J.[Junru],
Shi, H.[Humphrey],
Yan, Z.C.[Zhi-Cheng],
Wang, Z.Y.[Zhang-Yang],
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained
Neural Architecture Search,
WACV22(2354-2363)
IEEE DOI
2202
Computational modeling, Search methods,
X3D, Benchmark testing, Probabilistic logic,
Analysis and Understanding Deep Learning ->
Efficient Training and Inference Methods for Networks
BibRef
Chen, N.L.[Neng-Lun],
Chu, L.[Lei],
Pan, H.[Hao],
Lu, Y.[Yan],
Wang, W.P.[Wen-Ping],
Self-Supervised Image Representation Learning with Geometric Set
Consistency,
CVPR22(19270-19280)
IEEE DOI
2210
Image segmentation, Semantics, Training data, Object detection,
Image representation, Representation learning,
Self- semi- meta- unsupervised learning
BibRef
Lin, Y.Z.[Yuan-Ze],
Guo, X.[Xun],
Lu, Y.[Yan],
Self-Supervised Video Representation Learning with Meta-Contrastive
Network,
ICCV21(8219-8229)
IEEE DOI
2203
Training, Representation learning, Multitasking, Task analysis,
Transfer/Low-shot/Semi/Unsupervised Learning, Video analysis and understanding
BibRef
Guo, X.D.[Xu-Dong],
Guo, X.[Xun],
Lu, Y.[Yan],
SSAN: Separable Self-Attention Network for Video Representation
Learning,
CVPR21(12613-12622)
IEEE DOI
2111
Correlation, Pairwise error probability,
Computational modeling, Semantics, Cognition, Pattern recognition
BibRef
Yang, X.T.[Xi-Tong],
Fan, H.Q.[Hao-Qi],
Torresani, L.[Lorenzo],
Davis, L.S.[Larry S.],
Wang, H.[Heng],
Beyond Short Clips:
End-to-End Video-Level Learning with Collaborative Memories,
CVPR21(7563-7572)
IEEE DOI
2111
Training, Collaboration,
Predictive models, Fasteners, Pattern recognition
BibRef
Wu, C.Y.[Chao-Yuan],
Krähenbühl, P.[Philipp],
Towards Long-Form Video Understanding,
CVPR21(1884-1894)
IEEE DOI
2111
Visualization, Protocols, Computational modeling,
Machine vision, Benchmark testing
BibRef
Zhang, C.H.[Chu-Han],
Gupta, A.[Ankush],
Zisserman, A.[Andrew],
Temporal Query Networks for Fine-grained Video Understanding,
CVPR21(4484-4494)
IEEE DOI
2111
Training, Location awareness,
Pattern recognition, Videos
BibRef
Kangaspunta, J.[Juhana],
Piergiovanni, A.[AJ],
Jonschkowski, R.[Rico],
Ryoo, M.[Michael],
Angelova, A.[Anelia],
Adaptive Intermediate Representations for Video Understanding,
MULA21(1602-1612)
IEEE DOI
2109
Training, Visualization, Computational modeling,
Atmospheric modeling, Motion segmentation, Semantics, Performance gain
BibRef
Duan, H.D.[Hao-Dong],
Zhao, Y.[Yue],
Xiong, Y.J.[Yuan-Jun],
Liu, W.T.[Wen-Tao],
Lin, D.[Dahua],
Omni-sourced Webly-supervised Learning for Video Recognition,
ECCV20(XV:670-688).
Springer DOI
2011
BibRef
Jha, A.,
Kumar, A.,
Pande, S.,
Banerjee, B.,
Chaudhuri, S.,
MT-UNET: A Novel U-Net Based Multi-Task Architecture For Visual Scene
Understanding,
ICIP20(2191-2195)
IEEE DOI
2011
Task analysis, Decoding, Feature extraction, Semantics,
Loss measurement, Image segmentation, Estimation,
deep learning
BibRef
Diba, A.[Ali],
Fayyaz, M.[Mohsen],
Sharma, V.[Vivek],
Paluri, M.[Manohar],
Gall, J.[Jürgen],
Stiefelhagen, R.[Rainer],
Van Gool, L.J.[Luc J.],
Large Scale Holistic Video Understanding,
ECCV20(V:593-610).
Springer DOI
2011
BibRef
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
BibRef
Hu, A.[Anthony],
Cotter, F.[Fergal],
Mohan, N.[Nikhil],
Gurau, C.[Corina],
Kendall, A.[Alex],
Probabilistic Future Prediction for Video Scene Understanding,
ECCV20(XVI: 767-785).
Springer DOI
2010
BibRef
Mavroudi, E.[Effrosyni],
Haro, B.B.[Benjamín Béjar],
Vidal, R.[René],
Representation Learning on Visual-Symbolic Graphs for Video
Understanding,
ECCV20(XXIX: 71-90).
Springer DOI
2010
BibRef
Sener, F.[Fadime],
Singhania, D.[Dipika],
Yao, A.[Angela],
Temporal Aggregate Representations for Long-range Video Understanding,
ECCV20(XVI: 154-171).
Springer DOI
2010
BibRef
Tosi, F.,
Aleotti, F.,
Ramirez, P.Z.,
Poggi, M.,
Salti, S.,
di Stefano, L.,
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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Piergiovanni, A.J.,
Angelova, A.[Anelia],
Ryoo, M.S.[Michael S.],
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
BibRef
Xiong, Y.,
Huang, Q.,
Guo, L.,
Zhou, H.,
Zhou, B.,
Lin, D.,
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
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
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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
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
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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
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Skalic, M.[Miha],
Austin, D.[David],
Building A Size Constrained Predictive Models for Video Classification,
Large-Scale18(IV:297-305).
Springer DOI
1905
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Garg, S.[Shivam],
Learning Video Features for Multi-label Classification,
Large-Scale18(IV:325-337).
Springer DOI
1905
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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
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Kmiec, S.[Sebastian],
Bae, J.[Juhan],
An, R.J.[Rui-Jian],
Learnable Pooling Methods for Video Classification,
Large-Scale18(IV:229-238).
Springer DOI
1905
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Liu, T.Q.[Tian-Qi],
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
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Singh, K.[Kamaljeet],
Brox, T.[Thomas],
ECO: Efficient Convolutional Network for Online Video Understanding,
ECCV18(II: 713-730).
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1810
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Sah, S.,
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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
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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
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ICMR11(29).
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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 .