Résumé: Zero-shot learning (ZSL) requires one to associate visual and semantic information observed from data of seen classes, so that test data of unseen classes can be recognized based on the described semantic representation. Aiming at synthesizing visual data from the given semantic inputs, hallucination-based ZSL approaches might suffer from mode collapse and biased problems due to the lack of ability in modeling the desirable visual features for unseen categories. In this paper, we present a generative model of Cross-Modal Consistency GAN (CMC-GAN), which performs semantics-guided intra-category knowledge transfer across image categories, so that data hallucination for unseen classes can be achieved with proper semantics and sufficient visual diversity. In our experiments, we perform standard and generalized ZSL on four benchmark datasets, confirming the effectiveness of our approach over that of state-of-the-art ZSL methods.

Langue: Anglais