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Measuring concept generalization, i.e., the extent to which models trained on a set of (seen) visual concepts can be leveraged to recognize a new set of (unseen) concepts, is a popular way of evaluating visual representations, especially in a self-supervised learning framework.
Verb semantics and lexical selection
Zhibiao Wu and Martha Palmer · 1994
Earlier work this paper cites.
Wordnet: A lexical database for english
George A Miller · 1995
Earlier work this paper cites.
Using information content to evaluate semantic similarity in a taxonomy
Philip Resnik · 1995
Earlier work this paper cites.
Semantic similarity based on corpus statistics and lexical taxonomy
Jay Jiang and David Conrath · 1997
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An information-theoretic definition of similarity
Dekang Lin · 1998
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The PASCAL Visual Object Classes Challenge 2007 Results
Mark Everingham, Luc Van Gool, Christopher Williams, John Winn, and Andrew Zisserman · 2007
Earlier work this paper cites.
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Natural Language Processing with Python
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Earlier work this paper cites.
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Earlier work this paper cites.
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Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Visual and semantic similarity in imagenet
Thomas Deselaers and Vittorio Ferrari · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei Efros · 2011
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Elad Mezuman and Yair Weiss · 2012
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Distributed representations of words and phrases and their compositionality
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Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
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Zero-shot recognition with unreliable attributes
Dinesh Jayaraman and Kristen Grauman · 2014
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and Lawrence Zitnick · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
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Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Joint learning of the embedding of words and entities for named entity disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji · 2016
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Domain Adaptation in Computer Vision Applications
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Webvision database: Visual learning and understanding from web data
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester, and Nati Srebro · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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A new benchmark for evaluation of cross-domain few-shot learning
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Hard negative mixing for contrastive learning
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Online bag-of-visual-words generation for unsupervised representation learning
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A study of face obfuscation in ImageNet
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
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Barlow twins: Self-supervised learning via redundancy reduction
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What makes instance discrimination good for transfer learning?
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