Dual-level contrastive learning network for generalized zero-shot learning

Generalized zero-shot learning (GZSL) aims to utilize semantic information to recognize the seen and unseen samples, where unseen classes are unavailable during training. Though recent advances have been made by incorporating contrastive learning into GZSL, existing approaches still suffer from two limitations: (1) without considering fine-grained cluster structures, these models cannot guarantee the discriminability and semantic awareness of synthetic features; (2) classifiers tend to overfit the seen classes, as they only concentrate on the seen domain. To address these challenges, we propose a Dual-level Contrastive Learning Network (DCLN), in which intra-domain and cross-domain contrastive learning are seamlessly integrated into a unified learning model. Specifically, the former performs center-prototype contrasting to fully explore the discriminative structure knowledge, while the latter is proposed to effectively alleviate the overfitting problem by utilizing the semantic relationships between the seen and unseen domain. Finally, the experimental results on four benchmark datasets demonstrate the superiority of our DCLN over the state-of-the-art methods.