Abstract
Most existing Zero-Shot Learning (ZSL) methods have
the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen
(source) classes. So they yield poor performance after being deployed in the generalized ZSL settings. In this paper,
we propose a straightforward yet effective method named
Quasi-Fully Supervised Learning (QFSL) to alleviate the
bias problem. Our method follows the way of transductive
learning, which assumes that both the labeled source images and unlabeled target images are available for training.
In the semantic embedding space, the labeled source images
are mapped to several fixed points specified by the source
categories, and the unlabeled target images are forced to be
mapped to other points specified by the target categories.
Experiments conducted on AwA2, CUB and SUN datasets
demonstrate that our method outperforms existing state-ofthe-art approaches by a huge margin of 9.3 ? 24.5% following generalized ZSL settings, and by a large margin of
0.2 ? 16.2% following conventional ZSL settings