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％。

视觉增强动态语义原型用于生成式零样本学习

Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot  Learning

Generalized zero-shot learning (GZSL) has achieved significant progress, with
many efforts dedicated to overcoming the problems of visual-semantic domain gap
and seen-unseen bias. However, most existing methods directly use feature
extraction models trained on ImageNet alone, ignoring the cross-dataset bias
between ImageNet and GZSL benchmarks. Such a bias inevitably results in
poor-quality visual features for GZSL tasks, which potentially limits the
recognition performance on both seen and unseen classes. In this paper, we
propose a simple yet effective GZSL method, termed feature refinement for
generalized zero-shot learning (FREE), to tackle the above problem. FREE
employs a feature refinement (FR) module that incorporates
\textit{semantic$\rightarrow$visual} mapping into a unified generative model to
refine the visual features of seen and unseen class samples. Furthermore, we
propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a
semantic cycle-consistency loss to guide FR to learn class- and
semantically-relevant representations, and concatenate the features in FR to
extract the fully refined features. Extensive experiments on five benchmark
datasets demonstrate the significant performance gain of FREE over its baseline
and current state-of-the-art methods. Our codes are available at
this https URL .

本篇论文提出了一种名为 Feature Refinement for Generalized Zero-Shot Learning (FREE) 的简单有效的 GZSL 方法，其采用特征细化模块，将语义 - 视觉映射引入到生成模型中，以精炼已见和未见类别样本的视觉特征。实验结果表明，FREE 在五个基准数据集上表现优于当前最先进的方法。