Dual Adaptive Visual-Semantic Prompt Collaboration for Generalized Zero-Shot Learning

Abstract

Generalized zero-shot learning (GZSL) addresses the challenging task of recognizing both seen and unseen classes by leveraging shared semantic knowledge. A core challenge in this domain is achieving robust visual-semantic alignment to transfer knowledge from seen classes to novel classes. Current state-of-the-art methods typically fine-tune large-scale visual backbones on scarce training data. However, this approach frequently leads to severe overfitting to seen classes, which significantly degrades performance on novel categories. To mitigate this issue, we propose the Dual Adaptive Visual-Semantic Prompt Collaboration Network (VSPCN+), a novel framework that utilizes prompt-tuning for effective feature adaptation. Our method introduces a dual-prompt mechanism comprising both visual and semantic prompts. The semantic prompts guide the visual encoder to learn visual features that are more semantically consistent with class attributes, while the visual prompts steer the semantic encoder to generate semantic representations that are more visually grounded. This collaborative process enhances the overall visual-semantic consistency. A key innovation of our work is the dynamic generation of instance-adaptive prompts, which contrasts with existing prompt-learning methods that rely on static, global prompts. By tailoring prompts to individual instances, our approach enhances the model’s robustness and generalization capabilities across diverse visual inputs. This collaborative adaptation, guided by our dual-prompt mechanism, allows the visual and semantic encoders to produce consistent representations for effective visual-semantic alignment. Extensive experiments on standard GZSL benchmarks demonstrate that our proposed VSPCN+ performs favorably against several state-of-the-art methods.