Self-supervised Object Recognition
Self-Supervised Object Recognition is a machine learning paradigm within object recognition that leverages unlabeled data to generate supervisory signals from the input itself, enabling the model to learn discriminative features without explicit human-annotated labels.
49
Publications
4.2K
Citations
208
Authors
70
Institutions
Publications per year
2017–2026
40
Parents
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Relja Arandjelović, Andrew Zisserman · 2017 · 765 citations
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
Xinlong Wang, Rufeng Zhang, Chunhua Shen et al. · 2021 · 603 citations
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
Zheng Wu, Weiliang Tang, Li Jiang et al. · 2021 · 422 citations
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University of Oxford
Oxford, United Kingdom
5 h-index
10 publications
1K citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
11 publications
732 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
5 publications
329 citations
Google (United States)
Mountain View, United States
2 h-index
2 publications
161 citations

Harbin Institute of Technology
Harbin, China
2 h-index
4 publications
200 citations
Meta (Israel)
Tel Aviv, Israel
2 h-index
5 publications
1.3K citations
Jingdong (China)
Beijing, China
2 h-index
7 publications
388 citations
Google DeepMind (United Kingdom)
London, United Kingdom
2 h-index
4 publications
1.6K citations
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Repository · 15 publications · 718 citations
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Conference · 4 publications · 234 citations
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Conference · 2 publications · 334 citations
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Conference · 2 publications · 36 citations
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1 publications · 11 citations
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