Cross-domain mapping learning for transductive zero-shot learning

Abstract

Zero-shot learning (ZSL) aims to learn a projection function from a visual feature space to a semantic embedding space or reverse. The main challenge of ZSL is the domain shift problem where the unseen test data has a large gap with the seen training data. Transductive ZSL based methods alleviate this problem by learning from both labeled data and unlabeled data to capture their common semantic information. In this paper, we propose a framework to learn a robust cross-domain mapping for transductive ZSL with an extremely efficient algorithm for model optimization. Combining with a deep model, we formulate the cross-domain mapping as a general loss function that optimizes both the projection function and discriminative visual features simultaneously in an end-to-end manner. Extensive experiments on five benchmark datasets show that the proposed Cross-Domain Mapping (CDM) model outperforms the state-of-the-art.