DKGZSL: Leveraging Dynamic Visual-Semantic Knowledge for Generative Zero-Shot Learning

Generative Zero-Shot Learning (GZSL) methods address the challenge of recognizing unseen classes by synthesizing visual features, thereby converting ZSL into a supervised learning task. However, existing approaches are predominantly constrained to two multi-stage strategies: pre-generation prior knowledge enhancement and post-generation feature refinement. These paradigms often suffer from error propagation across stages, ultimately limiting generation quality and representational fidelity. To overcome these limitations, we propose DKGZSL, a novel generative framework that injects dynamic visual-semantic knowledge directly into the feature synthesis process, effectively unifying generation and refinement into a single cohesive stage. Specifically, a Knowledge Transfer Network (KTN) is introduced to convert semantic information into hierarchical visual knowledge representations. To ensure accurate semantic-visual alignment, we further design a Semantic-Oriented Visual Refinement (SOVR) module that reshapes real visual features into semantically aligned and noise-suppressed representations, providing precise guidance for the KTN. Moreover, hierarchical knowledge extracted from each KTN layer is progressively transmitted to the generator via Meta-Fusion Units (MFUs), enabling dynamic semantic guidance and improving generation quality. Extensive experiments on three benchmark datasets demonstrate that DKGZSL achieves consistent state-of-the-art performance with both ResNet-101 and ViT-B/16 feature extractors. Comprehensive ablation studies further confirm the effectiveness and complementarity of each proposed component. The code is available at https://github.com/JingHu-gdut/DKGZSL