  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 significantly outperforms state-of-the-art methods.
