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。