20.4.5.6.5 Video Understanding

Chapter Contents (Back)
Video Understanding.

Deep Video Understanding Dataset,
2020, used for workshops, and challenges. WWW Link.
Dataset, 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

Suresha, M., Kuppa, S., Raghukumar, D.S.,
A study on deep learning spatiotemporal models and feature extraction techniques for video understanding,
MultInfoRetr(9), No. 2, June 2020, pp. 81-101.
Springer DOI 2005
BibRef

Kavoosifar, M.R.[Mohammad Reza], Apiletti, D.[Daniele], Baralis, E.[Elena], Garza, P.[Paolo], Huet, B.[Benoit],
Effective video hyperlinking by means of enriched feature sets and monomodal query combinations,
MultInfoRetr(9), No. 3, September 2020, pp. 215-227.
Springer DOI 2008
BibRef

Tang, P.J.[Peng-Jie], Tan, Y.L.[Yun-Lan], Li, J.Z.[Jin-Zhong], Tan, B.[Bin],
Translating video into language by enhancing visual and language representations,
JVCIR(72), 2020, pp. 102875.
Elsevier DOI 2010
Video description, Feature enhancing, CNN, LSTM, Semantic BibRef

Yu, J., Jiang, X., Qin, Z., Zhang, W., Hu, Y., Wu, Q.,
Learning Dual Encoding Model for Adaptive Visual Understanding in Visual Dialogue,
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 representation learning,
IVC(104), 2020, pp. 103988.
Elsevier DOI 2012
Video representation learning, Long-range dependencies capturing, Video classification BibRef

Tan, H.L.[Hui Li], Zhu, H.Y.[Hong-Yuan], Lim, J.H.[Joo-Hwee], Tan, C.[Cheston],
A comprehensive survey of procedural video datasets,
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 for unsupervised video pre-training,
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 BibRef

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], Zhang, C.[Chao], Zhang, H.Y.[Hong-Yang],
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning,
PAMI(46), No. 3, March 2024, pp. 1362-1377.
IEEE DOI Code:
WWW Link. 2402
Training, Data models, Noise measurement, Deep learning, Predictive models, Neural networks, Self-supervised learning, robust learning under noise 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 BibRef

Hu, Y.[Yaosi], Yin, D.C.[Da-Cheng], Wang, Y.W.[Yu-Wang], Chen, Z.Z.[Zhen-Zhong], Luo, C.[Chong],
Decomposing style, content, and motion for videos,
JVCIR(89), 2022, pp. 103686.
Elsevier DOI 2212
Video decomposition, Video synthesis, Self-supervised learning BibRef

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.Y.[Tian-Yu], 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 BibRef

Schiappa, M.C.[Madeline C.], Rawat, Y.S.[Yogesh S.], Shah, M.[Mubarak],
Self-Supervised Learning for Videos: A Survey,
Surveys(55), No. 13s, July 2023, pp. xx-yy.
DOI Link 2309
Survey, Video Understanding. Survey, Self-Supervised Learning. video understanding, zero-shot learning, visual-language models, deep learning, multimodal learning BibRef

Yang, X.M.[Xing-Ming], Xiong, S.[Sixuan], Wu, K.W.[Ke-Wei], Shan, D.F.[Dong-Feng], Xie, Z.[Zhao],
Attentive spatial-temporal contrastive learning for self-supervised video representation,
IVC(137), 2023, pp. 104765.
Elsevier DOI 2309
Self-supervised learning, Spatial-temporal feature, Contrastive learning, Spatial-temporal self-attention BibRef

Miao, J.X.[Jia-Xu], Wei, Y.C.[Yun-Chao], Wang, X.H.[Xiao-Han], Yang, Y.[Yi],
Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset,
PAMI(45), No. 9, September 2023, pp. 11297-11308.
IEEE DOI 2309

WWW Link. BibRef

Hu, D.[Di], Wang, Z.[Zheng], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Self-Supervised Learning for Heterogeneous Audiovisual Scene Analysis,
MultMed(25), 2023, pp. 3534-3545.
IEEE DOI 2310
BibRef

Namitha, K.[Kalakunnath], Geetha, M.[Madathilkulangara], Athi, N.[Narayanan],
An Improved Interaction Estimation and Optimization Method for Surveillance Video Synopsis,
MultMedMag(30), No. 3, July 2023, pp. 25-36.
IEEE DOI 2310
BibRef

Assefa, M.[Maregu], Jiang, W.[Wei], Alemu, K.G.[Kumie Gedamu], Yilma, G.[Getinet], Adhikari, D.[Deepak], Ayalew, M.[Melese], Seid, A.M.[Abegaz Mohammed], Erbad, A.[Aiman],
Actor-Aware Self-Supervised Learning for Semi-Supervised Video Representation Learning,
CirSysVideo(33), No. 11, November 2023, pp. 6679-6692.
IEEE DOI Code:
WWW Link. 2311
BibRef

Hu, Y.F.[Yu-Fan], Gao, J.Y.[Jun-Yu], Xu, C.S.[Chang-Sheng],
Learning Multi-Expert Distribution Calibration for Long-Tailed Video Classification,
MultMed(26), 2024, pp. 555-567.
IEEE DOI 2402
Tail, Head, Calibration, Training, Data models, Task analysis, Visualization, Long-tailed distribution, video classification, multi-expert calibration BibRef

Chen, Z.[Ziyu], Wang, H.L.[Han-Li], Chen, C.W.[Chang Wen],
Self-Supervised Video Representation Learning by Serial Restoration With Elastic Complexity,
MultMed(26), 2024, pp. 2235-2248.
IEEE DOI 2402
Task analysis, Feature extraction, Representation learning, Manuals, Spatiotemporal phenomena, Image restoration, nearest neighbor retrieval BibRef

Chen, Z.L.[Zai-Long], Wang, L.[Lei], Wang, P.[Peng], Gao, P.[Peng],
Question-Aware Global-Local Video Understanding Network for Audio-Visual Question Answering,
CirSysVideo(34), No. 5, May 2024, pp. 4109-4119.
IEEE DOI 2405
Feature extraction, Visualization, Task analysis, Question answering (information retrieval), Data mining, Fuses, deep learning BibRef


Jamal, M.A.[Muhammad Abdullah], Mohareri, O.[Omid],
M33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding,
WACV24(2532-2542)
IEEE DOI 2404
Representation learning, Solid modeling, Semantic segmentation, Estimation, Focusing, Algorithms, Machine learning architectures, Biomedical / healthcare / medicine BibRef

Tian, Y.[Yuan], Lu, G.[Guo], Zhai, G.T.[Guang-Tao], Gao, Z.Y.[Zhi-Yong],
Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression,
ICCV23(13564-13576)
IEEE DOI 2401
BibRef

Li, K.C.[Kun-Chang], Wang, Y.L.[Ya-Li], He, Y.[Yinan], Li, Y.Z.[Yi-Zhuo], Wang, Y.[Yi], Wang, L.M.[Li-Min], Qiao, Y.[Yu],
UniFormerV2: Unlocking the Potential of Image ViTs for Video Understanding,
ICCV23(1632-1643)
IEEE DOI 2401
BibRef

Afham, M.[Mohamed], Shukla, S.N.[Satya Narayan], Poursaeed, O.[Omid], Zhang, P.[Pengchuan], Shah, A.[Ashish], Lim, S.[Sernam],
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding,
REDLCV23(1181-1186)
IEEE DOI 2401
BibRef

Strafforello, O.[Ombretta], Schutte, K.[Klamer], van Gemert, J.C.[Jan C.],
Are current long-term video understanding datasets long-term?,
CVEU23(2959-2968)
IEEE DOI 2401
BibRef

Zhao, Y.C.[Yu-Cheng], Luo, C.[Chong], Tang, C.X.[Chuan-Xin], Chen, D.D.[Dong-Dong], Codella, N.[Noel], Zha, Z.J.[Zheng-Jun],
Streaming Video Model,
CVPR23(14602-14612)
IEEE DOI 2309

WWW Link. BibRef

Maiya, S.R.[Shishira R], Girish, S.[Sharath], Ehrlich, M.[Max], Wang, H.Y.[Han-Yu], Lee, K.S.[Kwot Sin], Poirson, P.[Patrick], Wu, P.X.[Peng-Xiang], Wang, C.[Chen], Shrivastava, A.[Abhinav],
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-Wise Modeling,
CVPR23(14378-14387)
IEEE DOI 2309
BibRef

Zhang, Y.T.[Yi-Tian], Bai, Y.[Yue], Liu, C.[Chang], Wang, H.[Huan], Li, S.[Sheng], Fu, Y.[Yun],
Frame Flexible Network,
CVPR23(10504-10513)
IEEE DOI 2309

WWW Link. BibRef

Dessalene, E.[Eadom], Maynord, M.[Michael], Fermüller, C.[Cornelia], Aloimonos, Y.F.[Yi-Fannis],
Therbligs in Action: Video Understanding through Motion Primitives,
CVPR23(10618-10626)
IEEE DOI 2309
BibRef

Zhao, Y.[Yue], Misra, I.[Ishan], Krähenbühl, P.[Philipp], Girdhar, R.[Rohit],
Learning Video Representations from Large Language Models,
CVPR23(6586-6597)
IEEE DOI 2309
BibRef

Wang, R.[Rui], Chen, D.D.[Dong-Dong], Wu, Z.X.[Zu-Xuan], Chen, Y.P.[Yin-Peng], Dai, X.[Xiyang], Liu, M.C.[Meng-Chen], Yuan, L.[Lu], Jiang, Y.G.[Yu-Gang],
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning,
CVPR23(6312-6322)
IEEE DOI 2309
BibRef

Yang, X.T.[Xi-Tong], Chu, F.J.[Fu-Jen], Feiszli, M.[Matt], Goyal, R.[Raghav], Torresani, L.[Lorenzo], Tran, D.[Du],
Relational Space-Time Query in Long-Form Videos,
CVPR23(6398-6408)
IEEE DOI 2309
BibRef

Foo, L.G.[Lin Geng], Gong, J.[Jia], Fan, Z.P.[Zhi-Peng], Liu, J.[Jun],
System-Status-Aware Adaptive Network for Online Streaming Video Understanding,
CVPR23(10514-10523)
IEEE DOI 2309
BibRef

Dong, S.[Sixun], Hu, H.Z.[Hua-Zhang], Lian, D.Z.[Dong-Ze], Luo, W.X.[Wei-Xin], Qian, Y.C.[Yi-Cheng], Gao, S.H.[Sheng-Hua],
Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos,
CVPR23(2437-2447)
IEEE DOI 2309
BibRef

Wang, J.[Jue], Zhu, W.T.[Wen-Tao], Wang, P.[Pichao], Yu, X.[Xiang], Liu, L.[Linda], Omar, M.[Mohamed], Hamid, R.[Raffay],
Selective Structured State-Spaces for Long-Form Video Understanding,
CVPR23(6387-6397)
IEEE DOI 2309
BibRef

Zhang, H.[Heng], Liu, D.[Daqing], Zheng, Q.[Qi], Su, B.[Bing],
Modeling Video as Stochastic Processes for Fine-Grained Video Representation Learning,
CVPR23(2225-2234)
IEEE DOI 2309

WWW Link. BibRef

Kumar, Y.[Yogesh], Mishra, A.[Anand],
Few-Shot Referring Relationships in Videos,
CVPR23(2289-2298)
IEEE DOI 2309
BibRef

Harzig, P.[Philipp], Einfalt, M.[Moritz], Lienhart, R.[Rainer],
Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation,
ICIP22(2041-2045)
IEEE DOI 2211
Image coding, Video on demand, Art, Transformers, Synchronization, Machine translation, Task analysis, Video-to-text, Transformer, Audio-visual BibRef

Wiles, O.[Olivia], Carreira, J.[João], Barr, I.[Iain], Zisserman, A.[Andrew], Malinowski, M.[Mateusz],
Compressed Vision for Efficient Video Understanding,
ACCV22(VII:679-695).
Springer DOI 2307
BibRef

Rho, D.[Daniel], Cho, J.[Junwoo], Ko, J.H.[Jong Hwan], Park, E.[Eunbyung],
Neural Residual Flow Fields for Efficient Video Representations,
ACCV22(II:458-474).
Springer DOI 2307
BibRef

Tian, F.R.[Feng-Rui], Fan, J.W.[Jia-Wei], Yu, X.[Xie], Du, S.Y.[Shao-Yi], Song, M.[Meina], Zhao, Y.[Yu],
TCVM: Temporal Contrasting Video Montage Framework for Self-Supervised Video Representation Learning,
ACCV22(II:526-542).
Springer DOI 2307
BibRef

Huang, Z.M.[Zhi-Meng], Jia, C.M.[Chuan-Min], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei],
A Compressive Prior Guided Mask Predictive Coding Approach for Video Analysis,
ACCV22(IV:469-484).
Springer DOI 2307
BibRef

Li, L.[Li], Zhuang, L.S.[Lian-Sheng], Gao, S.H.[Sheng-Hua], Wang, S.[Shafei],
Havit: Hybrid-attention Based Vision Transformer for Video Classification,
ACCV22(IV:502-517).
Springer DOI 2307
BibRef

Zhang, H.L.[Huan-Le], Pirsiavash, H.[Hamed], Liu, X.[Xin],
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification,
WACV23(2507-2516)
IEEE DOI 2302
Computational modeling, Benchmark testing, Transformers, Algorithms: Machine learning architectures, formulations BibRef

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
BibRef

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

Ju, C.[Chen], 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.H.[Zi-Hua], 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

Voigtlaender, P.[Paul], Changpinyo, S.[Soravit], Pont-Tuset, J.[Jordi], Soricut, R.[Radu], Ferrari, V.[Vittorio],
Connecting Vision and Language with Video Localized Narratives,
CVPR23(2461-2471)
IEEE DOI 2309
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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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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
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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
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Sener, F.[Fadime], 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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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 BibRef

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


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