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Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes.
Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Learning to detect unseen object classes by betweenclass attribute transfer
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Label-embedding for attribute-based classification
Zeynep Akata, Florent Perronnin, Zaid Harchaoui, and Cordelia Schmid · 2013
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DeViSE: A deep visual-semantic embedding model
Andrea Frome, Greg Corrado, Jon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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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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Large-scale object classification using label relation graphs
Jia Deng, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam · 2014
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Attribute-based classification for zero-shot visual object categorization
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2014
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Zero-shot learning by convex combination of semantic embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, and Jeffrey Dean · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A. Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2014
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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
Jimmy Lei Ba, Kevin Swersky, Sanja Fidler, and Ruslan Salakhutdinov · 2015
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Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu and Marco Baroni · 2015
Zero-shot learning via semantic similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2015
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Synthesized classifiers for zero-shot learning
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha · 2016
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Semi-supervised vocabulary-informed learning
Yanwei Fu and Leonid Sigal · 2016
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Visual word2vec (vis-w2v): Learning visually grounded word embeddings using abstract scenes
Satwik Kottur, Ramakrishna Vedantam, Jose M. F. Moura, and Devi Parikh · 2016
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Attribute embedding with visual-semantic ambiguity removal for zero-shot learning
Yang Long, Li Liu, and Ling Shao · 2016
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Less is more: Zero-shot learning from online textual documents with noise suppression
Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, and Anton van den Hengel · 2016
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Prototypical priors: From improving classification to zero-shot learning
Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, and Philip H. S. Torr · 2015
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Combining language and vision with a multimodal skip-gram model
Angeliki Lazaridou, Nghia The Pham, and Marco Baroni · 2015
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Semi-supervised zero-shot classification with label representation learning
X. Li, Y. Guo, and D. Schuurmans · 2015
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip H.S. Torr · 2015
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Latent embeddings for zero-shot classification
Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, and Bernt Schiele · 2016
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Zero-shot learning via joint latent similarity embedding
Ziming Zhang and Venkatesh Saligrama · 2016
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From zero-shot learning to conventional supervised classification: Unseen visual data synthesis
Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, and Jungong Han · 2017
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