Most zero-shot learning (ZSL) methods aim to learn a mapping from visual feature space to semantic feature space or from both visual and semantic feature spaces to a common joint space and align them. However, in these methods the visual and semantic information are not utilized sufficiently and the useless information is not excluded. Moreover, there exists a strong bias problem that the instances from unseen classes always tend to be predicted as some seen classes in most ZSL methods. In this paper, combining the advantages of generative adversarial networks (GANs), a method based on bidirectional projections between the visual and semantic feature spaces is proposed. GANs are used to perform bidirectional generations and alignments between the visual and semantic features. In addition, cycle mapping structure ensures that the important information are kept in the alignments. Furthermore, in order to better solve the bias problem, pseudo-labels are generated for unseen instances and the model is adjusted along with them iteratively. We conduct extensive experiments at traditional ZSL and generalized ZSL settings, respectively. Experiment results confirm that our method achieves the state-of-the-art performances on the popular datasets AWA2, aPY and SUN.

大多数零镜头学习（ZSL）方法旨在学习从视觉特征空间到语义特征空间或从视觉和语义特征空间到公共关节空间的映射并将它们对齐。但是，在这些方法中，视觉和语义信息没有得到充分利用，并且无用信息也没有被排除。此外，存在一个严重的偏差问题，即在大多数ZSL方法中，总是将来自看不见类的实例总是预测为某些已见类。结合生成对抗网络的优点，提出了一种基于视觉和语义特征空间之间双向投影的方法。GAN用于执行视觉特征和语义特征之间的双向生成和对齐。此外，循环映射结构可确保重要信息保持一致。此外，为了更好地解决偏差问题，针对看不见的实例生成了伪标签，并对其进行了迭代调整。我们分别在传统的ZSL和广义ZSL设置下进行了广泛的实验。实验结果证实，我们的方法在流行的数据集AWA2，aPY和SUN上达到了最先进的性能。