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Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.
Cross-generalization: Learning novel classes from a single example by feature replacement
Evgeniy Bart and Shimon Ullman · 2005
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs · 2005
Earlier work this paper cites.
One-shot learning of object categories
Li Fei-Fei, Robert Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Describing objects by their attributes
a. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
Earlier work this paper cites.
Measuring invariances in deep networks
Ian Goodfellow, Honglak Lee, Quoc V Le, Andrew Saxe, and Andrew Y Ng · 2009
Earlier work this paper cites.
Logo retrieval with a contrario visual query expansion
Alexis Joly and Olivier Buisson · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
Earlier work this paper cites.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y Ng · 2009
Earlier work this paper cites.
Zero-shot learning with semantic output codes
Mark Palatucci, Dean Pomerleau, Geoffrey E Hinton, and Tom M Mitchell · 2009
Cited alongside, same era.
One shot learning of simple visual concepts
Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum · 2011
Cited alongside, same era.
Wsabie: Scaling up to large vocabulary image annotation
Jason Weston, Samy Bengio, and Nicolas Usunier · 2011
Cited alongside, same era.
Multi-column deep neural network for traffic sign classification
Dan Cireşan, Ueli Meier, Jonathan Masci, and Jürgen Schmidhuber · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Cited alongside, same era.
Sun attribute database: Discovering, annotating, and recognizing scene attributes
Genevieve Patterson and James Hays · 2012
Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Tomas Mikolov, et al · 2013
Later among the works it cites.
Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
Later among the works it cites.
Zero-shot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg S Corrado, and Jeffrey Dean · 2013
Later among the works it cites.
Zero-shot learning through cross-modal transfer
Richard Socher, Milind Ganjoo, Christopher D Manning, and Andrew Ng · 2013
Later among the works it cites.
Transfer learning based on the observation probability of each attribute
Masahiro Suzuki, Haruhiko Sato, Satoshi Oyama, and Masahito Kurihara · 2014
Later among the works it cites.
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Cited alongside, same era.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
Cited alongside, same era.
Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
Cited alongside, same era.
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes, ENG OX, and Philip HS Torr · 2015
Closest in time.
Learning visual similarity for product design with convolutional neural networks
Kavita Bala Sean Bell · 2015
Closest in time.