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Elsevier DOI
0804
Object recognition; Video database; Video understanding; Semantic
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BibRef
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Brostow, G.J.[Gabriel J.],
Pollefeys, M.[Marc],
Segmenting video into classes of algorithm-suitability,
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1006
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2005
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Springer DOI
2008
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Li, J.Z.[Jin-Zhong],
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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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
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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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IVC(104), 2020, pp. 103988.
Elsevier DOI
2012
Video representation learning,
Long-range dependencies capturing, Video classification
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Tan, H.L.[Hui Li],
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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
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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
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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
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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
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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
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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
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Huang, L.[Lang],
Zhang, C.[Chao],
Zhang, H.Y.[Hong-Yang],
Self-Adaptive Training: Bridging Supervised and Self-Supervised
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PAMI(46), No. 3, March 2024, pp. 1362-1377.
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WWW Link.
2402
Training, Data models, Noise measurement, Deep learning,
Predictive models, Neural networks, Self-supervised learning,
robust learning under noise
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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],
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
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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
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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
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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
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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
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WWW Link.
2311
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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.Y.[Zi-Yu],
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
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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
Cao, H.Z.[Hao-Zhi],
Xu, Y.C.[Yue-Cong],
Mao, K.Z.[Ke-Zhi],
Xie, L.H.[Li-Hua],
Yin, J.X.[Jian-Xiong],
See, S.[Simon],
Xu, Q.W.[Qian-Wen],
Yang, J.F.[Jian-Fei],
Self-Supervised Video Representation Learning by Video Incoherence
Detection,
Cyber(54), No. 6, June 2024, pp. 3810-3822.
IEEE DOI
2406
Spatiotemporal phenomena, Task analysis, Representation learning,
Cognition, Training, Self-supervised learning, Supervised learning,
video representation learning
BibRef
Zhang, Z.Q.[Zi-Qi],
Ma, Z.Y.[Zong-Yang],
Yuan, C.F.[Chun-Feng],
Chen, Y.X.[Yu-Xin],
Wang, P.[Peijin],
Qi, Z.A.[Zhong-Ang],
Hao, C.L.[Cheng-Lei],
Li, B.[Bing],
Shan, Y.[Ying],
Hu, W.M.[Wei-Ming],
Maybank, S.J.[Stephen J.],
Chinese Title Generation for Short Videos:
Dataset, Metric and Algorithm,
PAMI(46), No. 7, July 2024, pp. 5192-5208.
IEEE DOI
2406
Videos, Task analysis, Measurement, Semantics, Benchmark testing,
Electronic commerce, Annotations, Video and language,
text-video retrieval
BibRef
Bi, S.[Shuai],
Hu, Z.P.[Zheng-Ping],
Zhang, H.[Hehao],
Di, J.[Jirui],
Sun, Z.[Zhe],
Motion-guided spatiotemporal multitask feature discrimination for
self-supervised video representation learning,
PR(155), 2024, pp. 110713.
Elsevier DOI
2408
Unsupervised learning, Self-supervised learning,
Cross-view learning, Multitask discrimination, Video action understanding
BibRef
Li, D.[Dong],
Jin, J.D.[Jian-Dong],
Zhang, Y.H.[Yu-Hao],
Zhong, Y.L.[Yan-Lin],
Wu, Y.Y.[Yao-Yang],
Chen, L.[Lan],
Wang, X.[Xiao],
Luo, B.[Bin],
Semantic-aware frame-event fusion based pattern recognition via large
vision-language models,
PR(158), 2025, pp. 111080.
Elsevier DOI Code:
WWW Link.
2411
RGB-event fusion, Large vision-language models,
Semantic information, Pattern recognition
BibRef
Wu, W.H.[Wen-Hao],
Wang, X.H.[Xiao-Han],
Luo, H.P.[Hai-Peng],
Wang, J.D.[Jing-Dong],
Yang, Y.[Yi],
Ouyang, W.L.[Wan-Li],
Cap4Video++: Enhancing Video Understanding With Auxiliary Captions,
PAMI(47), No. 7, July 2025, pp. 5223-5237.
IEEE DOI
2506
Streams, Computational modeling, Visualization, Training, Semantics,
Transformers, Muitimodal learning, text-video retrieval,
video understanding
BibRef
Verma, D.[Dhruv],
Roy, D.[Debaditya],
Fernando, B.[Basura],
Effectively Leveraging CLIP for Generating Situational Summaries of
Images and Videos,
IJCV(133), No. 8, August 2025, pp. 5302-5325.
Springer DOI Code:
WWW Link.
2508
Summary of the situation or context.
BibRef
Song, X.[Xin],
Tian, W.[Wang],
Zhu, Q.Q.[Qi-Qi],
Zhang, X.L.[Xiang-Long],
VideoMamba++: Integrating state space model with dual attention for
enhanced video understanding,
IVC(161), 2025, pp. 105609.
Elsevier DOI
2509
Video action recognition, State space model,
Dual attention mechanism, Gated convolution
BibRef
Li, T.P.[Teng-Peng],
Wang, H.[Hanli],
Li, Q.[Qinyu],
Ni, Z.[Zhangkai],
Vision-Language Relational Transformer for Video-to-Text Generation,
MultMed(27), 2025, pp. 4584-4596.
IEEE DOI
2509
Visualization, Transformers, Feature extraction, Semantics,
Oral communication, Accuracy, Generators, Long short term memory,
visual relation modeling
BibRef
Wang, Q.H.[Qiu-Heng],
Shi, Y.K.[Yu-Kai],
Ou, J.[Jiarong],
Chen, R.[Rui],
Lin, K.[Ke],
Wang, J.H.[Jia-Hao],
Jiang, B.[Boyuan],
Yang, H.T.[Hao-Tian],
Zheng, M.[Mingwu],
Tao, X.[Xin],
Yang, F.[Fei],
Wan, P.F.[Peng-Fei],
Zhang, D.[Di],
Koala-36M: A Large-Scale Video Dataset Improving Consistency between
Fine-Grained Conditions and Video Content,
CVPR25(8428-8437)
IEEE DOI Code:
WWW Link.
2508
Training, Visualization, Accuracy, Filtering, Pipelines, Refining,
Data processing, Probability distribution, Quality assessment, video datasets
BibRef
Ho, D.[Darryl],
Madden, S.[Samuel],
DejaVid: Encoder-Agnostic Learned Temporal Matching for Video
Classification,
CVPR25(24023-24032)
IEEE DOI Code:
WWW Link.
2508
Training, Accuracy, Neural networks, Time series analysis,
Transformers, Feeds, Videos
BibRef
Manasyan, A.[Anna],
Seitzer, M.[Maximilian],
Radovic, F.[Filip],
Martius, G.[Georg],
Zadaianchuk, A.[Andrii],
Temporally Consistent Object-Centric Learning by Contrasting Slots,
CVPR25(5401-5411)
IEEE DOI
2508
Training, Dynamics, Object segmentation, Stability analysis,
Question answering (information retrieval), Planning, Synthetic data
BibRef
Bigverdi, M.[Mahtab],
Luo, Z.[Zelun],
Hsieh, C.Y.[Cheng-Yu],
Shen, E.[Ethan],
Chen, D.P.[Dong-Ping],
Shapiro, L.G.[Linda G.],
Krishna, R.[Ranjay],
Perception Tokens Enhance Visual Reasoning in Multimodal Language
Models,
CVPR25(3836-3845)
IEEE DOI
2508
Training, Ion radiation effects, Visualization, Computational modeling,
Depth measurement, Image representation, reasoning
BibRef
Ren, Z.W.[Zhong-Wei],
Wei, Y.C.[Yun-Chao],
Guo, X.[Xun],
Zhao, Y.[Yao],
Kang, B.[Bingyi],
Feng, J.S.[Jia-Shi],
Jin, X.J.[Xiao-Jie],
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos,
CVPR25(29029-29039)
IEEE DOI
2508
Training, Visualization, Knowledge acquisition,
Computational modeling, Large language models, Videos
BibRef
Tang, Y.L.[Yun-Long],
Guo, J.J.[Jun-Jia],
Hua, H.[Hang],
Liang, S.[Susan],
Feng, M.Q.[Ming-Qian],
Li, X.Y.[Xin-Yang],
Mao, R.[Rui],
Huang, C.[Chao],
Bi, J.[Jing],
Zhang, Z.L.[Ze-Liang],
Fazli, P.[Pooyan],
Xu, C.L.[Chen-Liang],
VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?,
CVPR25(8490-8500)
IEEE DOI Code:
WWW Link.
2508
Visualization, Annotations, Large language models,
Computational modeling, Benchmark testing, Cameras, Videos,
evaluation benchmark
BibRef
Kim, K.[Kangsan],
Park, G.[Geon],
Lee, Y.[Youngwan],
Yeo, W.[Woongyeong],
Hwang, S.J.[Sung Ju],
VideoICL: Confidence-based Iterative In-context Learning for
Out-of-Distribution Video Understanding,
CVPR25(3295-3305)
IEEE DOI Code:
WWW Link.
2508
Costs, Computational modeling, Refining, Training data,
Performance gain, Iterative methods, Faces, Videos
BibRef
Huang, Z.P.[Zhen-Peng],
Li, X.H.[Xin-Hao],
Li, J.Q.[Jia-Qi],
Wang, J.[Jing],
Zeng, X.Y.[Xiang-Yu],
Liang, C.[Cheng],
Wu, T.[Tao],
Chen, X.[Xi],
Li, L.[Liang],
Wang, L.M.[Li-Min],
Online Video Understanding: OVBench and VideoChat-Online,
CVPR25(3328-3338)
IEEE DOI
2508
Training, Systematics, Computational modeling, Benchmark testing,
Solids, Real-time systems, Spatiotemporal phenomena, Videos,
online video understanding
BibRef
Liu, B.[Benlin],
Dong, Y.H.[Yu-Hao],
Wang, Y.Q.[Yi-Qin],
Ma, Z.X.[Zi-Xian],
Tang, Y.S.[Yan-Song],
Tang, L.[Luming],
Rao, Y.M.[Yong-Ming],
Ma, W.C.[Wei-Chiu],
Krishna, R.[Ranjay],
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal
Language Model,
CVPR25(3783-3792)
IEEE DOI
2508
Training, Visualization, Solid modeling, Video tracking, Navigation,
Computational modeling, Benchmark testing, Cognition, Videos,
video understanding
BibRef
Zhao, Y.L.[Yi-Lun],
Zhang, H.[Haowei],
Xie, L.[Lujing],
Hu, T.Y.[Tong-Yan],
Gan, G.[Guo],
Long, Y.[Yitao],
Hu, Z.Y.[Zhi-Yuan],
Chen, W.Y.[Wei-Yuan],
Li, C.H.[Chu-Han],
Xu, Z.J.[Zhi-Jian],
Wang, C.Y.[Cheng-Ye],
Shangguan, Z.Y.[Zi-Yao],
Liang, Z.W.[Zhen-Wen],
Liu, Y.X.[Yi-Xin],
Zhao, C.[Chen],
Cohan, A.[Arman],
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding,
CVPR25(8475-8489)
IEEE DOI
2508
Foundation models, Error analysis, Computational modeling,
Social sciences, Pipelines, Benchmark testing, Cognition, Videos,
Visual perception
BibRef
Lv, B.X.[Bai-Xuan],
Zha, Y.H.[Yao-Hua],
Dai, T.[Tao],
Yuerong, X.[Xue],
Chen, K.[Ke],
Xia, S.T.[Shu-Tao],
Adapting Pre-trained 3D Models for Point Cloud Video Understanding
via Cross-frame Spatio-temporal Perception,
CVPR25(12413-12422)
IEEE DOI Code:
WWW Link.
2508
Point cloud compression, Training, Adaptation models,
Solid modeling, Computational modeling, Gesture recognition,
pre-trained model
BibRef
Ashutosh, K.[Kumar],
Nagarajan, T.[Tushar],
Pavlakos, G.[Georgios],
Kitani, K.[Kris],
Grauman, K.[Kristen],
ExpertAF: Expert Actionable Feedback from Video,
CVPR25(13582-13594)
IEEE DOI
2508
Training, Measurement, Visualization, Benchmark testing,
Artificial intelligence, Videos, Sports, video understanding,
expert feedback
BibRef
Yang, J.W.[Jian-Wei],
Tan, R.[Reuben],
Wu, Q.H.[Qian-Hui],
Zheng, R.J.[Rui-Jie],
Peng, B.L.[Bao-Lin],
Liang, Y.[Yongyuan],
Gu, Y.[Yu],
Cai, M.[Mu],
Ye, S.[Seonghyeon],
Jang, J.[Joel],
Deng, Y.[Yuquan],
Gao, J.F.[Jian-Feng],
Magma: A Foundation Model for Multimodal AI Agents,
CVPR25(14203-14214)
IEEE DOI
2508
Visualization, Navigation, Foundation models, Predictive models,
Reproducibility of results, Planning, Magma,
image and video understanding
BibRef
Hu, K.[Kai],
Gao, F.[Feng],
Nie, X.H.[Xiao-Han],
Zhou, P.[Peng],
Tran, S.[Son],
Neiman, T.[Tal],
Wang, L.Y.[Ling-Yun],
Shah, M.[Mubarak],
Hamid, R.[Raffay],
Yin, B.[Bing],
Chilimbi, T.[Trishul],
M-LLM Based Video Frame Selection for Efficient Video Understanding,
CVPR25(13702-13712)
IEEE DOI
2508
Visualization, Pain, Annotations, Large language models,
Benchmark testing, Question answering (information retrieval), Videos
BibRef
Jung, M.[Minjoon],
Xiao, J.B.[Jun-Bin],
Zhang, B.T.[Byoung-Tak],
Yao, A.[Angela],
On the Consistency of Video Large Language Models in Temporal
Comprehension,
CVPR25(13713-13722)
IEEE DOI Code:
WWW Link.
2508
Codes, Grounding, Large language models, Robustness, Probes, Tuning,
Standards, Videos, video large language models, video understanding
BibRef
Li, C.[Chaoyu],
Im, E.W.[Eun Woo],
Fazli, P.[Pooyan],
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large
Language Models for Video Understanding,
CVPR25(13723-13733)
IEEE DOI Code:
WWW Link.
2508
Visualization, Codes, Large language models, Buildings,
Benchmark testing, Cognition, Videos, hallucination, benchmark
BibRef
Liu, J.[Jiuming],
Han, J.[Jinru],
Liu, L.[Lihao],
Aviles-Rivero, A.I.[Angelica I.],
Jiang, C.[Chaokang],
Liu, Z.[Zhe],
Wang, H.S.[He-Sheng],
Mamba4D: Efficient 4D Point Cloud Video Understanding with
Disentangled Spatial-Temporal State Space Models,
CVPR25(17626-17636)
IEEE DOI Code:
WWW Link.
2508
Point cloud compression, Correlation, Semantic segmentation,
Scalability, Video sequences, Memory management, Transformers, Videos
BibRef
Das, S.[Sarmistha],
Mujavarsheik, B.[Basha],
Lyngkhoi, R.E.Z.[R. E. Zera],
Saha, S.[Sriparna],
Maurya, A.[Alka],
Deciphering the Complaint Aspects: Towards an Aspect-Based Complaint
Identification Model with Video Complaint Dataset in Finance,
WACV25(7195-7204)
IEEE DOI
2505
Measurement, Image segmentation, Analytical models,
Image resolution, Reviews, Standards organizations, Companies,
Image restoration
BibRef
Liu, H.[Hong],
Nakashima, Y.[Yuta],
Babaguchi, N.[Noboru],
Paladin: Understanding Video Intentions in Political Advertisement
Videos,
WACV25(8239-8248)
IEEE DOI
2505
Instruments, Computational modeling, Decision making, Psychology,
Natural language processing, video understanding, benchmark
BibRef
Bae, K.[Kyungho],
Ahn, G.[Geo],
Kim, Y.[Youngrae],
Choi, J.[Jinwoo],
DEVIAS: Learning Disentangled Video Representations of Action and Scene,
ECCV24(LXVIII: 431-448).
Springer DOI
2412
Code:
WWW Link.
BibRef
Salehi, M.[Mohammadreza],
Dorkenwald, M.[Michael],
Thoker, F.M.[Fida Mohammad],
Gavves, E.[Efstratios],
Snoek, C.G.M.[Cees G. M.],
Asano, Y.M.[Yuki M.],
Sigma: Sinkhorn-guided Masked Video Modeling,
ECCV24(XXIV: 293-312).
Springer DOI
2412
Code:
WWW Link.
BibRef
Xie, B.Z.[Bin-Zhu],
Zhang, S.C.[Si-Cheng],
Zhou, Z.[Zitang],
Li, B.[Bo],
Zhang, Y.H.[Yuan-Han],
Hessel, J.[Jack],
Yang, J.K.[Jing-Kang],
Liu, Z.W.[Zi-Wei],
FUNQA: Towards Surprising Video Comprehension,
ECCV24(I: 39-57).
Springer DOI
2412
BibRef
Choi, M.[Minkyu],
Goel, H.[Harsh],
Omama, M.[Mohammad],
Yang, Y.H.[Yun-Hao],
Shah, S.[Sahil],
Chinchali, S.[Sandeep],
Towards Neuro-symbolic Video Understanding,
ECCV24(LXXVIII: 220-236).
Springer DOI
2412
BibRef
Fan, Y.[Yue],
Ma, X.J.[Xiao-Jian],
Wu, R.J.[Ru-Jie],
Du, Y.T.[Yun-Tao],
Li, J.Q.[Jia-Qi],
Gao, Z.[Zhi],
Li, Q.[Qing],
Videoagent: A Memory-augmented Multimodal Agent for Video Understanding,
ECCV24(XXII: 75-92).
Springer DOI
2412
BibRef
Wang, S.J.[Shi-Jie],
Zhao, Q.[Qi],
Do, M.Q.[Minh Quan],
Agarwal, N.[Nakul],
Lee, K.[Kwonjoon],
Sun, C.[Chen],
VAMOS: Versatile Action Models for Video Understanding,
ECCV24(XII: 142-160).
Springer DOI
2412
BibRef
Lebreton, P.[Pierre],
Le Callet, P.[Patrick],
Birkbeck, N.[Neil],
Wang, Y.L.[Yi-Lin],
Adsumilli, B.[Balu],
A Dataset for Understanding Open UGC Video Datasets,
ICIP24(165-171)
IEEE DOI Code:
WWW Link.
2411
Training, Open Access, User-generated content, Merging,
Training data, Streaming media, Predictive models, UGC video, datasets
BibRef
Wu, J.T.[Jian-Tao],
Mo, S.T.[Shen-Tong],
Atito, S.[Sara],
Feng, Z.H.[Zhen-Hua],
Kittler, J.V.[Josef V.],
Husain, S.S.[Syed Sameed],
Awais, M.[Muhammad],
Masked Momentum Contrastive Learning for Semantic Understanding by
Observation,
ICIP24(263-269)
IEEE DOI
2411
Visualization, Image segmentation, Thresholding (Imaging),
Protocols, Zero-shot learning, Large language models, Semantics,
zero-shot segmentation
BibRef
Yun, H.[Hoyeoung],
Ahn, J.[Jinwoo],
Kim, M.[Minseo],
Kim, E.S.[Eun-Sol],
Compositional Video Understanding with Spatiotemporal Structure-based
Transformers,
CVPR24(18751-18760)
IEEE DOI
2410
Learning systems, Visualization, Semantics,
Transformers, Distance measurement
BibRef
Papalampidi, P.[Pinelopi],
Koppula, S.[Skanda],
Pathak, S.[Shreya],
Chiu, J.[Justin],
Heyward, J.[Joe],
Patraucean, V.[Viorica],
Shen, J.J.[Jia-Jun],
Miech, A.[Antoine],
Zisserman, A.[Andrew],
Nematzdeh, A.[Aida],
A Simple Recipe for Contrastively Pre-Training Video-First Encoders
Beyond 16 Frames,
CVPR24(14386-14397)
IEEE DOI
2410
Training, Visualization, Memory management, Benchmark testing, Encoding
BibRef
Wang, A.D.[An-Dong],
Wu, B.[Bo],
Chen, S.[Sunli],
Chen, Z.F.[Zhen-Fang],
Guan, H.T.[Hao-Tian],
Lee, W.N.[Wei-Ning],
Li, L.E.[Li Erran],
Gan, C.[Chuang],
SOK-Bench: A Situated Video Reasoning Benchmark with Aligned
Open-World Knowledge,
CVPR24(13384-13394)
IEEE DOI
2410
Visualization, Quality assurance, Reviews, Scalability, Manuals,
Benchmark testing, commonsense reasoning
BibRef
Zhong, Y.[Yang],
Baghel, B.K.[Bhiman Kumar],
Multimodal Understanding of Memes with Fair Explanations,
MULA24(2007-2017)
IEEE DOI
2410
Social networking (online), Computational modeling,
Benchmark testing, Cultural differences, Fairness
BibRef
Sheng, D.[Dianmo],
Chen, D.D.[Dong-Dong],
Tan, Z.T.[Zhen-Tao],
Liu, Q.[Qiankun],
Chu, Q.[Qi],
Bao, J.M.[Jian-Min],
Gong, T.[Tao],
Liu, B.[Bin],
Xu, S.W.[Sheng-Wei],
Yu, N.H.[Neng-Hai],
Towards More Unified In-Context Visual Understanding,
CVPR24(13362-13372)
IEEE DOI
2410
Visualization, Quantization (signal), Semantic segmentation,
Large language models, Pipelines, Transformers, Multitasking
BibRef
Ma, F.[Fan],
Jin, X.J.[Xiao-Jie],
Wang, H.[Heng],
Xian, Y.C.[Yu-Chen],
Feng, J.S.[Jia-Shi],
Yang, Y.[Yi],
Vista-llama: Reducing Hallucination in Video Language Models via
Equal Distance to Visual Tokens,
CVPR24(13151-13160)
IEEE DOI Code:
WWW Link.
2410
Visualization, Attention mechanisms, Accuracy,
Large language models, Computational modeling, Benchmark testing,
Video Understanding
BibRef
Tan, C.L.[Chao-Lei],
Lai, J.H.[Jian-Huang],
Zheng, W.S.[Wei-Shi],
Hu, J.F.[Jian-Fang],
Siamese Learning with Joint Alignment and Regression for
Weakly-Supervised Video Paragraph Grounding,
CVPR24(13569-13580)
IEEE DOI
2410
Location awareness, Grounding, Annotations, Semantics,
Semisupervised learning, Transformers, siamese learning,
video understanding
BibRef
Jin, P.[Peng],
Takanobu, R.[Ryuichi],
Zhang, W.[Wancai],
Cao, X.C.[Xiao-Chun],
Yuan, L.[Li],
Chat-UniVi: Unified Visual Representation Empowers Large Language
Models with Image and Video Understanding,
CVPR24(13700-13710)
IEEE DOI Code:
WWW Link.
2410
Bridges, Visualization, Codes, Large language models,
Computational modeling, Oral communication, Video Understanding
BibRef
Chalk, J.[Jacob],
Huh, J.[Jaesung],
Kazakos, E.[Evangelos],
Zisserman, A.[Andrew],
Damen, D.[Dima],
TIM: A Time Interval Machine for Audio-Visual Action Recognition,
CVPR24(18153-18163)
IEEE DOI Code:
WWW Link.
2410
Measurement, Visualization, Adaptation models, Codes, Accuracy,
Computational modeling, action recognition, action detection,
video understanding
BibRef
Wang, J.[Junke],
Chen, D.D.[Dong-Dong],
Luo, C.[Chong],
He, B.[Bo],
Yuan, L.[Lu],
Wu, Z.X.[Zu-Xuan],
Jiang, Y.G.[Yu-Gang],
OmniViD: A Generative Framework for Universal Video Understanding,
CVPR24(18209-18220)
IEEE DOI Code:
WWW Link.
2410
Training, Location awareness, Visualization, Vocabulary,
Benchmark testing, Robustness
BibRef
Zeng, R.[Runhao],
Chen, X.Y.[Xiao-Yong],
Liang, J.M.[Jia-Ming],
Wu, H.[Huisi],
Cao, G.Z.[Guang-Zhong],
Guo, Y.[Yong],
Benchmarking the Robustness of Temporal Action Detection Models
Against Temporal Corruptions,
CVPR24(18263-18274)
IEEE DOI Code:
WWW Link.
2410
Training, Location awareness, Source coding, Benchmark testing,
Feature extraction, Transformers, Temporal Action Detection,
Video Understanding
BibRef
Peirone, S.A.[Simone Alberto],
Pistilli, F.[Francesca],
Alliegro, A.[Antonio],
Averta, G.[Giuseppe],
A Backpack Full of Skills: Egocentric Video Understanding with
Diverse Task Perspectives,
CVPR24(18275-18285)
IEEE DOI
2410
Streaming media, Benchmark testing,
Egocentric Vision, Video Understanding
BibRef
Nguyen, T.T.[Trong-Thuan],
Nguyen, P.[Pha],
Luu, K.[Khoa],
HIG: Hierarchical Interlacement Graph Approach to Scene Graph
Generation in Video Understanding,
CVPR24(18384-18394)
IEEE DOI
2410
Visualization, Scalability, Computational modeling,
Benchmark testing, Task analysis, ASPIRe
BibRef
Tores, J.[Julie],
Sassatelli, L.[Lucile],
Wu, H.Y.[Hui-Yin],
Bergman, C.[Clement],
Andolfi, L.[Léa],
Ecrement, V.[Victor],
Precioso, F.[Frédéric],
Devars, T.[Thierry],
Guaresi, M.[Magali],
Julliard, V.[Virginie],
Lecossais, S.[Sarah],
Visual Objectification in Films: Towards a New AI Task for Video
Interpretation,
CVPR24(10864-10874)
IEEE DOI
2410
Representation learning, Visualization, Codes,
Computational modeling, Psychology, Motion pictures, objectification
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
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
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
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
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
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, 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,
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
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
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
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,
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
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],
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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
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Springer DOI
2010
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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,
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,
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
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.L.[Pei-Lin],
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.J.[Rui-Jian],
Learnable Pooling Methods for Video Classification,
Large-Scale18(IV:229-238).
Springer DOI
1905
BibRef
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
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
Long Video Understanding, Long-Form Video Understandint .