  Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained
models as their feature extractors, which are usually trained on the ImageNet
data set by using deep neural networks. The richness of the feature information
embedded in the pre-trained models can help the ZSL model extract more useful
features from its limited training samples. However, sometimes the difference
between the training data set of the current ZSL task and the ImageNet data set
is too large, which may lead to the use of pre-trained models has no obvious
help or even negative impact on the performance of the ZSL model. To solve this
problem, this paper proposes a biologically inspired feature enhancement
framework for ZSL. Specifically, we design a dual-channel learning framework
that uses auxiliary data sets to enhance the feature extractor of the ZSL model
and propose a novel method to guide the selection of the auxiliary data sets
based on the knowledge of biological taxonomy. Extensive experimental results
show that our proposed method can effectively improve the generalization
ability of the ZSL model and achieve state-of-the-art results on three
benchmark ZSL tasks. We also explained the experimental phenomena through the
way of feature visualization.
