Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary

Zero-shot learning for visual recognition, which approaches identifying unseen categories through a shared visual-semantic function learned on the seen categories and is expected to well adapt to unseen categories, has received considerable research attention most recently. However, the semantic gap between discriminant visual features and their underlying semantics is still the biggest obstacle, because there usually exists domain disparity across the seen and unseen classes. To deal with this challenge, we design two-stage generative adversarial networks to enhance the generalizability of semantic dictionary through low-rank embedding for zero-shot learning. In detail, we formulate a novel framework to simultaneously seek a two-stage generative model and a semantic dictionary to connect visual features with their semantics under a low-rank embedding. Our first-stage generative model is able to augment more semantic features for the unseen classes, which are then used to generate more discriminant visual features in the second stage, to expand the seen visual feature space. Therefore, we will be able to seek a better semantic dictionary to constitute the latent basis for the unseen classes based on the augmented semantic and visual data. Finally, our approach could capture a variety of visual characteristics from seen classes that are "ready-to-use" for new classes. Extensive experiments on four zero-shot benchmarks demonstrate that our proposed algorithm outperforms the state-of-the-art zero-shot algorithms.