Abstract
With the development of deep learning, zero-shot learning (ZSL) issues deserve more attention. Due to the problems of projection domain shift and discriminative feature extraction, we propose an end-to-end framework, which is different from traditional ZSL methods in the following two aspects: (1) we use a cascaded network to automatically locate discriminative regions, which can better extract latent features and contribute to the representation of key semantic attributes. (2) our framework achieves mapping in visual-semantic embedding space and calculation procedure of the dot product in deep learning framework. In addition, a joint loss function is designed for the regularization constraint of the whole method and achieves supervised learning, which enhances generalization ability in test set. In this paper, we make some experiments on Animals with Attributes 2 (AwA2), Caltech-UCSD Birds 200-2011 (CUB) and SUN datasets, which achieves better results compared to the state-of-the-art methods.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61772508, 61801428, U1713213), National Key R&D Program of China (2017YFB1402100), Zhejiang Provincial Natural Science Foundation (LY18F020034), Natural Science Basic Research Plan in Shaanxi Province of China (2017JM6101, 2017JM6060, 2017JQ6077, 2017JM6103), Guangdong Technology Project (2016B010108010, 2016B010125003, 2017B010110007), CAS Key Technology Talent Program, Shenzhen Engineering Laboratory for 3D Content Generating Technologies ([2017]476), Shenzhen Technology Project (JCYJ 20170413152535587, JSGG20160331185256983, JSGG20160229115709109), Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, CAS (2014DP173025), Fundamental Research Funds for the Central Universities (GK201703060, GK201801004), Teaching Reform and Research Project of Shaanxi Normal University (17JG33).
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Tian, Y., Zhang, W., Zhang, Q., Cheng, J., Hao, P., Lu, G. (2018). Co-consistent Regularization with Discriminative Feature for Zero-Shot Learning. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_4
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