Discriminative Learning of Latent Features for Zero-Shot Recognition

Yan Li, Junge Zhang, Jianguo Zhang, Kaiqi Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)
104 Downloads (Pure)

Abstract

Zero-shot learning (ZSL) aims to recognize unseen image categories by learning an embedding space between image and semantic representations. For years, among existing works, it has been the center task to learn the proper mapping matrices aligning the visual and semantic space, whilst the importance to learn discriminative representations for ZSL is ignored. In this work, we retrospect existing methods and demonstrate the necessity to learn discriminative representations for both visual and semantic instances of ZSL. We propose an end-to-end network that is capable of 1) automatically discovering discriminative regions by a zoom network; and 2) learning discriminative semantic representations in an augmented space introduced for both user-defined and latent attributes. Our proposed method is tested extensively on two challenging ZSL datasets, and the experiment results show that the proposed method signifi- cantly outperforms state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference Computer Vision and Pattern Recognition
PublisherIEEE
Pages7463-7471
Number of pages9
ISBN (Electronic)9781539661000
ISBN (Print)9781538664209
Publication statusPublished - 2018

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Li, Y., Zhang, J., Zhang, J., & Huang, K. (2018). Discriminative Learning of Latent Features for Zero-Shot Recognition. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (pp. 7463-7471). [2812] IEEE.