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.

本文提出了一种基于耦合字典学习的零样本学习方法，该方法通过利用种类原型中的区分性信息来对齐视觉-语义结构，进而提高不太具有区分性的语义空间的表现，并通过简单的最近邻方法在不同空间上执行零样本识别。在四个基准数据集上的实验证明了该方法的有效性。

通过结构对齐学习类别原型以实现零样本识别