  Although zero-shot learning (ZSL) has an inferential capability of
recognizing new classes that have never been seen before, it always faces two
fundamental challenges of the cross modality and crossdomain challenges. In
order to alleviate these problems, we develop a generative network-based ZSL
approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and
Taxonomy Regularization (TR). In our approach, the semantic features are taken
as inputs, and the output is the synthesized visual features generated from the
corresponding semantic features. CKL enables more relevant semantic features to
be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy
Regularization (TR) significantly improves the intersections with unseen images
with more generalized visual features generated from generative network.
Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB
and aPY) show that our approach is superior to these state-of-the-art methods
in terms of ZSL image classification and retrieval.
