Video Self-supervised Learning
Video Self-Supervised Learning is a machine learning paradigm that enables models to learn spatiotemporal representations from unlabeled video data by leveraging the inherent temporal coherence and structure of video sequences as supervisory signals.
142
Publications
10.8K
Citations
572
Authors
206
Institutions
Publications per year
2017–2026
135
Parents
142
VideoBERT: A Joint Model for Video and Language Representation Learning
Chen Sun, Austin Myers, Carl Vondrick et al. · 2019 · 1.1K citations
Self-Supervised Video Representation Learning with Odd-One-Out Networks
Basura Fernando, Hakan Bilen, Efstratios Gavves et al. · 2017 · 455 citations · Full text
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Limin Wang, Bingkun Huang, Zhiyu Zhao et al. · 2023 · 415 citations
1–5 of 142
572
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h-index
Institut national de recherche en sciences et technologies du numérique, France
5 publications · 1.3K citations
5
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5
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5
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5
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1–5 of 572
206
University of Oxford
Oxford, United Kingdom
11 h-index
24 publications
2.5K citations
Google (United States)
Mountain View, United States
8 h-index
35 publications
9K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
23 publications
1.7K citations
Tencent (China)
Shenzhen, China
7 h-index
23 publications
1.3K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
18 publications
1.5K citations
Meta (Israel)
Tel Aviv, Israel
5 h-index
20 publications
3.6K citations

University of Toronto
Toronto, Canada
4 h-index
11 publications
368 citations

Zhejiang University
Hangzhou, China
4 h-index
12 publications
346 citations
1–8 of 206
Repository · 25 publications · 1.5K citations
16
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Conference · 13 publications · 1.1K citations
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Conference · 10 publications · 354 citations
10
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Conference · 7 publications · 623 citations
7
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5 publications · 478 citations
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1–5