  Zero-shot learning (ZSL) aims to recognize objects of novel classes without
any training samples of specific classes, which is achieved by exploiting the
semantic information and auxiliary datasets. Recently most ZSL approaches focus
on learning visual-semantic embeddings to transfer knowledge from the auxiliary
datasets to the novel classes. However, few works study whether the semantic
information is discriminative or not for the recognition task. To tackle such
problem, we propose a coupled dictionary learning approach to align the
visual-semantic structures using the class prototypes, where the discriminative
information lying in the visual space is utilized to improve the less
discriminative semantic space. Then, zero-shot recognition can be performed in
different spaces by the simple nearest neighbor approach using the learned
class prototypes. Extensive experiments on four benchmark datasets show the
effectiveness of the proposed approach.
