Zero-shot Object Recognition
Zero-Shot Object Recognition is an object recognition paradigm that enables the identification of object categories without prior visual examples by leveraging semantic representations and prior knowledge to infer unseen classes.
66
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14.7K
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203
Authors
79
Institutions
Publications per year
2017–2026
48
Parents
66
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy et al. · arXiv (Cornell University) · 2021 · 5.3K citations · Full text
DeViSE: A Deep Visual-Semantic Embedding Model
Andrea Frome, Greg S. Corrado, Jon Shlens et al. · 2013 · 2.1K citations
Devi Parikh, Kristen Grauman · 2011 · 900 citations
Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo · arXiv (Cornell University) · 2013 · 811 citations · Full text
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Australian National University
Canberra, Australia
7 h-index
19 publications
1K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
16 publications
485 citations
Nanjing University of Science and Technology
Nanjing, China
5 h-index
17 publications
1.6K citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
5 h-index
9 publications
473 citations

Data61
Sydney, Australia
5 h-index
10 publications
512 citations
Inception Institute of Artificial Intelligence
Abu Dhabi, United Arab Emirates
4 h-index
4 publications
258 citations
Institute of Computing Technology
Beijing, China
3 h-index
9 publications
202 citations
University of Southern California
Los Angeles, United States
3 h-index
7 publications
2.1K citations
1–8 of 79
Repository · 16 publications · 6.6K citations
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Conference · 5 publications · 207 citations
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TPAMI · 3 publications · 1.7K citations
3
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2 publications · 48 citations
2
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2 publications · 71 citations
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