Although Zero Shot Learning (ZSL) has attracted more and more attention due to its powerful ability of recognizing new objects without retraining, it has a serious drawback that it only focuses on unseen classes during prediction. To solve this issue, Generalized ZSL (GZSL) extends the search range to both seen and unseen classes, which makes it a more realistic and challenging task. Conventional methods on GZSL often suffer from the domain shift problem on seen classes because they have only seen data for training. Deep Calibration Network (DCN) tries to minimize the entropy of assigning seen data to unseen classes to balance the training on both seen and unseen classes. However, there are still two problems for DCN, one is the hubness problem and another is the lack of training guidance. In this paper, to solve the two problems, we propose a novel method called PSeudo Distribution (PSD), which exploits the attribute similarity between seen classes and unseen classes as the training guidance to assign the seen data to unseen classes. In addition, the attribute similarity is also compressed to one-hot vector to further encourage the certainty of the model. Besides, the visual space is utilized as the embedding space, which can well settle the hubness problem. Extensive experiments are conducted on four popular datasets, and the results show the superiority of the proposed method.

尽管零射击学习（ZSL）由于其无需重新训练即可识别新物体的强大功能而引起了越来越多的关注，但它具有一个严重的缺点，即它仅在预测期间专注于看不见的课程。为了解决此问题，广义ZSL（GZSL）将搜索范围扩展到可见和不可见的类，这使其成为一项更加现实和更具挑战性的任务。GZSL上的常规方法通常会在看到的类上遭受域移位问题，因为它们只看到用于训练的数据。深度校准网络（DCN）试图最大程度地减少将可见数据分配给不可见班级的熵，以平衡对可见和不可见班级的训练。但是，DCN仍然存在两个问题，一个是中心性问题，另一个是缺乏培训指导。在本文中，要解决两个问题，我们提出了一种称为PSeudo分布（PSD）的新颖方法，该方法利用可见类和不可见类之间的属性相似性作为训练指南，将可见数据分配给不可见类。另外，属性相似度也被压缩为一热向量，以进一步促进模型的确定性。此外，视觉空间被用作嵌入空间，可以很好地解决中心度问题。在四个流行的数据集上进行了广泛的实验，结果表明了该方法的优越性。属性相似度也被压缩为一热向量，以进一步鼓励模型的确定性。此外，视觉空间被用作嵌入空间，可以很好地解决中心度问题。在四个流行的数据集上进行了广泛的实验，结果表明了该方法的优越性。属性相似度也被压缩为一热向量，以进一步鼓励模型的确定性。此外，视觉空间被用作嵌入空间，可以很好地解决中心度问题。在四个流行的数据集上进行了广泛的实验，结果表明了该方法的优越性。