  Zero-shot learning (ZSL) aims to infer novel classes without training samples
by transferring knowledge from seen classes. Existing embedding-based
approaches for ZSL typically employ attention mechanisms to locate attributes
on an image. However, these methods often ignore the complex entanglement among
different attributes' visual features in the embedding space. Additionally,
these methods employ a direct attribute prediction scheme for classification,
which does not account for the diversity of attributes in images of the same
category. To address these issues, we propose a novel Dual Feature Augmentation
Network (DFAN), which comprises two feature augmentation modules, one for
visual features and the other for semantic features. The visual feature
augmentation module explicitly learns attribute features and employs cosine
distance to separate them, thus enhancing attribute representation. In the
semantic feature augmentation module, we propose a bias learner to capture the
offset that bridges the gap between actual and predicted attribute values from
a dataset's perspective. Furthermore, we introduce two predictors to reconcile
the conflicts between local and global features. Experimental results on three
benchmarks demonstrate the marked advancement of our method compared to
state-of-the-art approaches. Our code is available at
https://github.com/Sion1/DFAN.
