  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.
