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Co-consistent Regularization with Discriminative Feature for Zero-Shot Learning

  • Yanling Tian
  • Weitong Zhang
  • Qieshi ZhangEmail author
  • Jun Cheng
  • Pengyi Hao
  • Gang Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

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.

Keywords

Zero-shot learning (ZSL) Discriminative region Regularization Projection domain shift Supervised learning 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yanling Tian
    • 1
    • 2
    • 3
  • Weitong Zhang
    • 1
    • 2
    • 3
  • Qieshi Zhang
    • 1
    • 2
    Email author
  • Jun Cheng
    • 1
    • 2
  • Pengyi Hao
    • 4
  • Gang Lu
    • 3
  1. 1.Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.The Chinese University of Hong KongHong KongChina
  3. 3.School of Computer ScienceShaanxi Normal UniversityXi’anChina
  4. 4.Zhejiang University of TechnologyHangzhouChina

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