Generative Zero-shot learning (ZSL) learns a generator to synthesize visual
samples for unseen classes, which is an effective way to advance ZSL. However,
existing generative methods rely on the conditions of Gaussian noise and the
predefined semantic prototype, which limit the generator only optimized on
specific seen classes rather than characterizing each visual instance,
resulting in poor generalizations (\textit{e.g.}, overfitting to seen classes).
To address this issue, we propose a novel Visual-Augmented Dynamic Semantic
prototype method (termed VADS) to boost the generator to learn accurate
semantic-visual mapping by fully exploiting the visual-augmented knowledge into
semantic conditions. In detail, VADS consists of two modules: (1) Visual-aware
Domain Knowledge Learning module (VDKL) learns the local bias and global prior
of the visual features (referred to as domain visual knowledge), which replace
pure Gaussian noise to provide richer prior noise information; (2)
Vision-Oriented Semantic Updation module (VOSU) updates the semantic prototype
according to the visual representations of the samples. Ultimately, we
concatenate their output as a dynamic semantic prototype, which serves as the
condition of the generator. Extensive experiments demonstrate that our VADS
achieves superior CZSL and GZSL performances on three prominent datasets and
outperforms other state-of-the-art methods with averaging increases by 6.4\%,
5.9\% and 4.2\% on SUN, CUB and AWA2, respectively.

通过充分利用视觉增强知识进入语义条件，我们提出了一种名为 VADS 的新型视觉增强动态语义原型方法，用于增强生成器学习准确的语义 - 视觉映射，实验证明我们的方法在三个知名数据集上取得了卓越的 CZSL 和 GZSL 性能，并且优于其他最先进方法，平均增加了 6.4％、5.9％和 4.2％。