Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by
designing a series of unbiased visual-semantics mappings, wherein, the
precision relies heavily on the completeness of extracted visual features from
both seen and unseen classes. However, as a common practice in GZSL, the
pre-trained feature extractor may easily exhibit difficulty in capturing
domain-specific traits of the downstream tasks/datasets to provide fine-grained
discriminative features, i.e., domain bias, which hinders the overall
recognition performance, especially for unseen classes. Recent studies
partially address this issue by fine-tuning feature extractors, while may
inevitably incur catastrophic forgetting and overfitting issues. In this paper,
we propose a simple yet effective Attribute-Aware Representation Rectification
framework for GZSL, dubbed $\mathbf{(AR)^{2}}$, to adaptively rectify the
feature extractor to learn novel features while keeping original valuable
features. Specifically, our method consists of two key components, i.e.,
Unseen-Aware Distillation (UAD) and Attribute-Guided Learning (AGL). During
training, UAD exploits the prior knowledge of attribute texts that are shared
by both seen/unseen classes with attention mechanisms to detect and maintain
unseen class-sensitive visual features in a targeted manner, and meanwhile, AGL
aims to steer the model to focus on valuable features and suppress them to fit
noisy elements in the seen classes by attribute-guided representation learning.
Extensive experiments on various benchmark datasets demonstrate the
effectiveness of our method.

我们提出了一种简单而有效的属性感知表征修正框架，用于广义零样本学习（GZSL），命名为（AR）^2，通过自适应地矫正特征提取器来学习新的特征并保留原有的有价值的特征。