  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.
