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Zero-Shot Learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones.
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Y. Shigeto, I. Suzuki, and K. Hara, “Ridge Regression, Hubness, and Zero-Shot Learning,” in Eur. Conf. Mach. Learn. , Porto, Portugal, Sep. 2015, pp. 135-151
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2015
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B. Marco, L. Angeliki, and D. Georgiana, “Hubness and pollution: Delving into cross-space mapping for zero-shot learning,” Proc. ACL. , 2015, pp. 270-280
2015
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2015
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A. Majumder, L. Behera, and V. Subramanian, “Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion,” IEEE Trans. Cybern. , vol. 48, no. 1, pp. 103 - 114, 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. on Comput. Vis. Pattern Recognit. , Las Vegas, USA, June 2016, pp. 770-778
2016
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W. L. Chao, S. Changpinyo, B. Gong, and F. Sha, “An empirical study and analysis of generalized zero-shot l earning for object recognition in the wild,” in Eur. Conf. on Comput. Vis. , Amsterdam, Netherlands, Oct. 2016, pp. 52-68
2016
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Y. Fu and L. Sigal, “Semi-supervised Vocabulary-informed Learning,” in Proc. Comput. Visi. Pattern Recognit. , Las Vegas, USA, June 2016, pp. 5337-5346
2016
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Z. Zhang and V. Saligrama, “Zero-shot learning via joint latent similarity embedding,” in Proc. Comput. Visi. Pattern Recognit. , Las Vegas, USA, June 2016, pp. 6034-6042
2016
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2017
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Y. Yu, Z. Ji, J. Guo, and Y. Pang, “Transductive zero-shot learning with adaptive structural embedding,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1-12, 2017
2017
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2017
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E. Kodirov, T. Xiang, and S. Gong, “Semantic Autoencoder for Zero-Shot Learning,” in Proc. Comput. Visi. Pattern Recognit. , 2017
2017
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X. Xu, F. Shen, Y. Yang, et al , “Matrix tri-factorization with manifold regularizations for zero-shot learning,” in Proc. IEEE Conf. on Comput. Vis. Pattern Recognit. , Honolulu, Hawaii, USA, July, 2017
2017
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Q. Wang and K. Chen, “Zero-Shot Visual Recognition via Bidirectional Latent Embedding,” in Int. J. Comput. Visi. , vol. 124, no. 3, pp. 356-383, 2017
2017
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D. Zhang, D. Meng, and J. Han, “Co-saliency detection via a self-paced multiple-instance learning framework,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 5, 2017, pp. 865-878
2017
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X. Yao, J. Han, D. Zhangm and F. Nie, “Revisiting co-saliency detection: A novel approach based on two-stage multi-view spectral rotation co-clustering,” IEEE Trans. on Image Process. , vol. 26, no. 7, 2017, pp. 3196-3209
2017
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Z. Zeng, Z. Li, D. Cheng, H. Zhang, K. Zhan, Y. Yang, “Two-Stream Multi-Rate Recurrent Neural Network for Video-Based Pedestrian Re-Identification,” IEEE Trans. on Ind. Inf. , 2017, pp. 1-8
2017
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2017
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2017
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G. Cheng, C. Yang, X. Yao, L. Guo, J. Han, “When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs,” IEEE Trans. on Geoscience and Remote Sensing , 2018, pp. 1-11
2018
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Y. Yu, Z. Ji, X. Li, J. Guo, et al. , “Transductive zero-shot learning with a self-training dictionary approach,” IEEE Trans. Cybern. , 2018, pp. 1-12
2018
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J. Han, D. Zhang, G. Cheng, N. Liu, and D. Xu, “Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey,” IEEE Signal Proc. Magazine , vol. 35, no. 1, 2018, pp. 84-100
2018
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