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Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen category based on the training instances from seen categories, in which the gap between seen categories and unseen categories is generally bridged via visual-semantic mapping between the low-level visual feature space and the intermediate semantic space.
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Z. Fu, T. Xiang, E. Kodirov, and S. Gong, “Zero-shot object recognition by semantic manifold distance,” in Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition , Boston, MA, Jun. 2015, pp. 2635–2644
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J. Lei Ba, K. Swersky, S. Fidler et al. , “Predicting deep zero-shot convolutional neural networks using textual descriptions,” in Proceedings of the 15th International Conference on Computer Vision , Santiago, Chile, Dec. 2015, pp. 4247–4255
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2016
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S. Changpinyo, W.-L. Chao, B. Gong, and F. Sha, “Synthesized classifiers for zero-shot learning,” in Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition , Las Vegas, NV, Jun. 2016, pp. 5327–5336
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Z. Zhang and V. Saligrama, “Zero-shot recognition via structured prediction,” in Proceedings of the 14th European Conference on Computer Vision , Amsterdam, The Netherlands, Oct. 2016, pp. 533–548
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W.-L. Chao, S. Changpinyo, B. Gong, and F. Sha, “An empirical study and analysis of generalized zero-shot learning for object recognition in the wild,” in Proceedings of the 14th European Conference on Computer Vision , Amsterdam, The Netherlands, Oct. 2016, pp. 52–68
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Y. Guo, G. Ding, J. Han, and Y. Gao, “Zero-shot learning with transferred samples,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3277–3290, 2017
2017
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X. Xu, T. Hospedales, and S. Gong, “Transductive zero-shot action recognition by word-vector embedding,” International Journal of Computer Vision , vol. 123, no. 3, pp. 309–333, 2017
2017
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E. Kodirov, T. Xiang, and S. Gong, “Semantic autoencoder for zero-shot learning,” Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition , Jul. 2017
2017
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S. Changpinyo, W.-L. Chao, and F. Sha, “Predicting visual exemplars of unseen classes for zero-shot learning,” ICCV , Oct. 2017
2017
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C. Luo, Z. Li, K. Huang, J. Feng, and M. Wang, “Zero-shot learning via attribute regression and class prototype rectification,” IEEE Transactions on Image Processing , vol. 27, no. 2, pp. 637–648, 2018
2018
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