Joint Concept Matching based Learning for Zero-Shot Recognition
This addresses the domain shift issue in zero-shot recognition for machine learning applications, representing an incremental improvement.
The paper tackles the projection domain shift problem in zero-shot learning by proposing a model that projects visual and semantic features into a common latent space with class-specific knowledge and reconstructs features to narrow the gap. It demonstrates superior performance over state-of-the-art methods on four benchmark datasets.
Zero-shot learning (ZSL) which aims to recognize unseen object classes by only training on seen object classes, has increasingly been of great interest in Machine Learning, and has registered with some successes. Most existing ZSL methods typically learn a projection map between the visual feature space and the semantic space and mainly suffer which is prone to a projection domain shift primarily due to a large domain gap between seen and unseen classes. In this paper, we propose a novel inductive ZSL model based on projecting both visual and semantic features into a common distinct latent space with class-specific knowledge, and on reconstructing both visual and semantic features by such a distinct common space to narrow the domain shift gap. We show that all these constraints on 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 to state-of-the-art algorithms.