Learning Similarity-Specific Dictionary For Zero-Shot Fine-Grained Recognition

In this paper, we study the problem of zero-shot fine-grained recognition. It aims to distinguish unseen subordinate categories through some other seen categories within an entry-level category. We demonstrate the necessity to learn multiple latent dictionaries through joint training with specific set of instances, human-defined attributes and the class labels. A novel approach that is capable of 1) automatically assigning suitable dictionaries for each instance and 2) learning similarity-specific semantic representations for zero-shot fine-grained recognition is proposed. Experimental results on three benchmark datasets demonstrate that the proposed method achieves superior or comparable performance.