  Zero-shot learning (ZSL) aims to recognize unseen classes by exploiting
semantic descriptions shared between seen classes and unseen classes. Current
methods show that it is effective to learn visual-semantic alignment by
projecting semantic embeddings into the visual space as class prototypes.
However, such a projection function is only concerned with seen classes. When
applied to unseen classes, the prototypes often perform suboptimally due to
domain shift. In this paper, we propose to learn prototypes via placeholders,
termed LPL, to eliminate the domain shift between seen and unseen classes.
Specifically, we combine seen classes to hallucinate new classes which play as
placeholders of the unseen classes in the visual and semantic space. Placed
between seen classes, the placeholders encourage prototypes of seen classes to
be highly dispersed. And more space is spared for the insertion of
well-separated unseen ones. Empirically, well-separated prototypes help
counteract visual-semantic misalignment caused by domain shift. Furthermore, we
exploit a novel semantic-oriented fine-tuning to guarantee the semantic
reliability of placeholders. Extensive experiments on five benchmark datasets
demonstrate the significant performance gain of LPL over the state-of-the-art
methods. Code is available at https://github.com/zaiquanyang/LPL.
