Hierarchical Coupled Discriminative Dictionary Learning for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize images of novel classes, but does not use any images belonging to the novel classes during model training, which is realized by exploiting the auxiliary semantic information. Recently, most ZSL methods focus on learning visual-semantic embeddings to transfer knowledge from the seen classes to the novel classes. Visual-semantic embedding is usually established based on the visual features of images and the semantic information of classes, i.e., class attributes. However, image features are extracted at the individual level, while class attributes are obtained at the group level, so the granularity of these features is different, which makes it difficult to match the two kinds of features. To tackle such problem, we propose hierarchical coupled discriminative dictionary learning (HCDDL) method to hierarchically establish visual-semantic embedding at class-level and image-level with a coarse-to-fine way. Firstly, a class-level coupled dictionary is trained to build basic and coarse-grained connection between visual space and semantic space. Using the class-level coupled dictionary, image attributes are generated. Based on the fine-grained image attributes and images features, an image-level coupled dictionary is learned. In addition, during the learning of hierarchical coupled dictionaries, the discriminative losses are adopted to ensure dictionaries learn more accurate representation, which is beneficial to the recognition task. Recognition of unseen images is performed through searching the class nearest to the unseen image in multiple spaces. Experiments on four widely used benchmark datasets show the effectiveness of the proposed method, and sufficient ablation experiments demonstrate that the coarse-to-fine way leads to good performances.