Zero-shot classification methods have attracted considerable attention in recent years. Existing ZSC methods encounter domain shift, hubness and visual–semantic gap problems. To address these problems, we propose a low-rank embedded orthogonal subspace learning method (LEOSL) for ZSC. Many previous works project visual features to the semantic space. However, they often suffer from the visual–semantic gap problem. To handle this problem, we project the visual representations and semantic representations to the common subspace. To address the domain shift problem, we restrict the mapping functions with a low-rank constraint. To handle the hubness problem, we introduce the class similarity term so that samples of the same class are located near each other, while samples of different classes are located far away. Furthermore, we restrict the shared representations in the subspace with an orthogonal constraint to remove the correlation between samples. The results show the superiority of LEOSL compared to many state-of-the-art methods.

零镜头分类方法近年来引起了相当大的关注。现有的ZSC方法会遇到域移位，中心度和视觉语义间隙问题。为了解决这些问题，我们提出了一种用于ZSC的低秩嵌入式正交子空间学习方法（LEOSL）。许多以前的作品将视觉特征投射到语义空间。但是，他们经常遭受视觉语义鸿沟问题的困扰。为了解决这个问题，我们将视觉表示和语义表示投影到公共子空间。为了解决域移位问题，我们以低秩约束来限制映射函数。为了处理中心度问题，我们引入类相似性术语，以使同一类的样本彼此靠近，而不同类的样本彼此远离。此外，我们使用正交约束来限制子空间中的共享表示，以消除样本之间的相关性。结果表明，LEOSL与许多最新方法相比具有优越性。