Attribute Affinity Coordination Debiasing for Generalized Zero-shot Learning

Generalized Zero-Shot Learning (GZSL) relies on visual-semantic mappings to recognize unseen categories by transferring knowledge from seen classes. However, the domain transfer process often introduces bias, which significantly hinders the model’s generalization ability on unseen classes. Existing GZSL methods struggle to balance debiasing with model performance, frequently adopting simplistic strategies that limit transfer effectiveness. To address this issue, we propose an Attribute Affinity Coordinated Debiasing (AACD) framework. AACD models the correlations among category-level attribute descriptions to capture latent inter-class affinity structures, thereby identifying and correcting incorrect knowledge transfer. Specifically, AACD enhances visual-semantic interactions via attribute-informed domain adaptation, incorporates an Affinity Discriminant Module (ADM) to guide the embedding space through similarity-based affinity discrimination, and introduces an Affinity Constraint Module (ACM) to reinforce intra-class consistency while maximizing inter-class separability. These modules are jointly integrated into the encoder to reduce the over-reliance on seen categories and promote robust domain transfer. Extensive experiments on three standard benchmarks CUB, SUN, and AwA2 demonstrate that our AACD framework achieves substantial improvements. Codes are available at https://github.com/QC-Elord/AACD.