Self-supervised Image Representations
Self-Supervised Image Representations is a methodological framework in computer vision that learns discriminative visual features from unlabeled images by formulating pretext tasks that leverage the intrinsic structure of the data to generate supervisory signals.
327
Publications
40.2K
Citations
1.5K
Authors
421
Institutions
Publications per year
2017–2026
320
Parents
327
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi et al. · arXiv (Cornell University) · 2020 · 7.3K citations · Full text
A simple framework for contrastive learning of visual representations
Ting Chen · TIB Data Manager · 2024 · 1.2K citations · Full text
BEiT: BERT Pre-Training of Image Transformers
Hangbo Bao, Dong Li · arXiv (Cornell University) · 2021 · 927 citations · Full text
1–5 of 327
1.5K
7
h-index
5
h-index
University of California, San Francisco, United States
5 publications · 295 citations
5
h-index
5
h-index
5
h-index
1–5 of 1.5K
421
Google (United States)
Mountain View, United States
17 h-index
86 publications
50.2K citations

Chinese Academy of Sciences
Beijing, China
11 h-index
37 publications
1.3K citations
University of Chinese Academy of Sciences
Beijing, China
10 h-index
30 publications
1.1K citations
University of Oxford
Oxford, United Kingdom
10 h-index
26 publications
3.8K citations
University of Science and Technology of China
Hefei, China
8 h-index
24 publications
885 citations
Meta (Israel)
Tel Aviv, Israel
8 h-index
22 publications
30.8K citations

Peking University
Beijing, China
8 h-index
22 publications
1.7K citations

Shanghai Jiao Tong University
Shanghai, China
7 h-index
23 publications
1.3K citations
1–8 of 421
Repository · 83 publications · 15.7K citations
34
h-index
Conference · 18 publications · 1.5K citations
17
h-index
Conference · 15 publications · 6.1K citations
14
h-index
Conference · 12 publications · 421 citations
12
h-index
8 publications · 1.1K citations
8
h-index
1–5