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Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided.
On the algorithmic implementation of multiclass kernel-based vector machines
K. Crammer and Y. Singer · 2002
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Stochastic neighbor embedding
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Learning with kernels: support vector machines, regularization, optimization, and beyond
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Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
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Convex multi-task feature learning
A. Argyriou, T. Evgeniou, and M. Pontil · 2008
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Liblinear: A library for large linear classification
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Describing objects by their attributes
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
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Learning to detect unseen object classes by between-class attribute transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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Zero-shot learning with semantic output codes
M. Palatucci, D. Pomerleau, G. E. Hinton, and T. M. Mitchell · 2009
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What helps where–and why? semantic relatedness for knowledge transfer
M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele · 2010
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Relative attributes
D. Parikh and K. Grauman · 2011
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Evaluating knowledge transfer and zero-shot learning in a large-scale setting
M. Rohrbach, M. Stark, and B. Schiele · 2011
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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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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Label-embedding for attribute-based classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2013
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Write a classifier: Zero-shot learning using purely textual descriptions
M. Elhoseiny, B. Saleh, and A. Elgammal · 2013
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Devise: A deep visual-semantic embedding model
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, and T. Mikolov · 2013
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. S. Corrado, and J. Dean · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Zero-shot learning through cross-modal transfer
R. Socher, M. Ganjoo, C. D. Manning, and A. Ng · 2013
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A unified probabilistic approach modeling relationships between attributes and objects
X. Wang and Q. Ji · 2013
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Designing category-level attributes for discriminative visual recognition
F. X. Yu, L. Cao, R. S. Feris, J. R. Smith, and S.-F. Chang · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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Evaluation of output embeddings for fine-grained image classification
Z. Akata, S. Reed, D. Walter, H. Lee, and B. Schiele · 2015
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How to transfer? zero-shot object recognition via hierarchical transfer of semantic attributes
Z. Al-Halah and R. Stiefelhagen · 2015
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Transductive multi-view zero-shot learning
Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong · 2015
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Zero-shot object recognition by semantic manifold distance
Z. Fu, T. Xiang, E. Kodirov, and S. Gong · 2015
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Active transfer learning with zero-shot priors: Reusing past datasets for future tasks
E. Gavves, T. Mensink, T. Tommasi, C. G. M. Snoek, and T. Tuytelaars · 2015
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Transductive multi-view embedding for zero-shot recognition and annotation
Y. Fu, T. M. Hospedales, T. Xiang, Z. Fu, and S. Gong · 2014
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Zero-shot recognition with unreliable attributes
D. Jayaraman and K. Grauman · 2014
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Decorrelating semantic visual attributes by resisting the urge to share
D. Jayaraman, F. Sha, and K. Grauman · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2014
Cited alongside, same era.
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Unsupervised domain adaptation for zero-shot learning
E. Kodirov, T. Xiang, Z. Fu, and S. Gong · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
J. Lei Ba, K. Swersky, S. Fidler, and R. salakhutdinov · 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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Unsupervised learning of neural network outputs
Y. Lu · 2015
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An embarrassingly simple approach to zero-shot learning
B. Romera-Paredes and P. H. S. Torr · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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A unified perspective on multi-domain and multi-task learning
Y. Yang and T. M. Hospedales · 2015
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Classifying unseen instances by learning class-independent similarity functions
Z. Zhang and V. Saligrama · 2015
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Zero-shot learning via semantic similarity embedding
Z. Zhang and V. Saligrama · 2015
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