Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition
This work addresses zero-shot recognition for novel classes without training samples, but it appears incremental as it builds on existing visual-semantic embedding methods.
The paper tackled the problem of zero-shot learning by proposing a coupled dictionary learning approach to align visual-semantic structures using class prototypes, improving discriminative information in the semantic space; experiments on four benchmark datasets demonstrated its effectiveness.
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.