Unsupervised Object Recognition
Unsupervised Object Recognition is a methodological framework within computer vision that enables the identification and localization of objects in visual data without reliance on labeled training examples, distinguishing itself from supervised approaches by leveraging inherent data structures, statistical regularities, or clustering mechanisms to discover object categories autonomously.
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17.6K
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289
Authors
117
Institutions
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2017–2026
30
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What is the best multi-stage architecture for object recognition?
Kevin Jarrett, Koray Kavukcuoglu, M. Ranzato et al. · 2009 · 2.2K citations
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University of Oxford
Oxford, United Kingdom
12 h-index
29 publications
6.2K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
14 publications
6.2K citations
Google (United States)
Mountain View, United States
6 h-index
13 publications
995 citations
University of California, Berkeley
Berkeley, United States
5 h-index
7 publications
6.9K citations
California Institute of Technology
Pasadena, United States
5 h-index
10 publications
5K citations
Microsoft (United States)
Redmond, United States
4 h-index
5 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
4 h-index
11 publications
584 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
3 h-index
6 publications
486 citations
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Repository · 15 publications · 1.2K citations
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TPAMI · 7 publications · 1.9K citations
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3 publications · 111 citations
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1 publications · 18 citations
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1 publications · 190 citations
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