Joint Concept Matching-Space Projection Learning for Zero-Shot Recognition
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Zero-shot learning (ZSL) has been widely researched and achieved a great success in machine learning, which aims to recognize unseen object classes by only training on seen object classes. Most existing ZSL methods are typically to learn a projection function between visual feature space and semantic space and mainly suffer a projection domain shift problem, as there is often a large domain gap between seen and unseen classes. In this paper, we proposed a novel inductive ZSL model based on project both visual and semantic features into a common distinct latent space with class-specific knowledge and reconstruct both visual and semantic features by such a distinct common space to narrow the domain shift gap. We show that all these constraints of the latent space, class-specific knowledge, reconstruction of features and their combinations enhance the robustness against the projection domain shift problem and improve the generalization ability to unseen object classes. Comprehensive experiments on four benchmark datasets demonstrate that our proposed method is superior than state-of-the-art algorithms.
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