Attribute disentanglement and re-entanglement for generalized zero-shot learning

The key challenge in zero-shot learning is inferring latent semantic knowledge between visual and attribute features of seen classes to achieve knowledge transfer to unseen classes. To address the limitation that local attribute features can only ensure attribute-level recognition rather than classification of an entire class, some methods incorporate global information into the process or results of local features extraction for classification. However, these approaches have not effectively addressed the issue. To address these issues, we propose an Attribute Disentanglement and Re-entanglement for Generalized Zero-Shot Learning. Our model no longer implicitly or explicitly incorporates global information into local attribute features for classification. Instead, we adjust local attribute features to make them more suitable for classification in re-entanglement phase, while ensuring the correct extraction of these features in disentanglement phase. We employ appropriate optimization loss functions and achieve significant improvements on three challenging benchmark datasets. Compared to other similar methods, our model exhibits strong competitiveness.