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-of-the-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.

本文提出了一种名为准完全监督学习（QFSL）的方法，通过转导学习的方式进行训练，解决了现有零样本学习方法中强偏差问题，并在 AwA2、CUB 和 SUN 数据集上进行了实验，表明我们的方法在广义 ZSL 设置下胜过现有的最先进方法 9.3〜24.5％，在常规的 ZSL 设置下则高出 0.2〜16.2％的显著优势。