Zero-Shot Learning (ZSL) has gained growing attention over the past few years mostly because it provides a significant scalability to recognition models for classifying instances from new unobserved classes. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier with lower cost, compared to collecting a new training set. Because seen and unseen classes are completely disjoint, ZSL methods often suffer from domain shift problem that occurs in transferring the knowledge of seen classes to unseen ones. Moreover, hubness problem that usually arises in high-dimensional space is another challenge in most ZSL methods due to applying nearest neighbor search for classification. To address these issues, a kernelized distance function is learned in order to discriminate the classes with a customized large-margin loss function. Furthermore, a simple theoretical-based prototype learning approach is provided by defining a non-linear mapping function to learn the visual prototype of each class from associated semantic information. For classification task, the learned distance function is utilized to measure the distance between instances and class-related prototypes. The evaluation on five benchmarks demonstrates the superiority of the proposed method over the state-of-the-art approaches in both zero-shot and generalized zero-shot learning problems.

零样本学习 (ZSL) 在过去几年中受到越来越多的关注，主要是因为它为识别模型提供了显着的可扩展性，用于从新的未观察到的类中对实例进行分类。这种可扩展性是通过提供有关新类的语义信息来实现的，与收集新的训练集相比，可以更容易地以更低的成本获得。因为可见类和不可见类是完全不相交的，ZSL 方法经常遇到域转移问题，这种问题在将可见类的知识转移到不可见类时发生。此外，由于应用最近邻搜索进行分类，通常在高维空间中出现的中心问题是大多数 ZSL 方法中的另一个挑战。为了解决这些问题，学习核化距离函数，以便用定制的大范围损失函数区分类别。此外，通过定义非线性映射函数来从相关语义信息中学习每个类的视觉原型，提供了一种简单的基于理论的原型学习方法。对于分类任务，学习到的距离函数用于测量实例与类相关原型之间的距离。对五个基准的评估证明了所提出的方法在零样本和广义零样本学习问题中优于最先进的方法。通过定义非线性映射函数来从相关语义信息中学习每个类的视觉原型，提供了一种简单的基于理论的原型学习方法。对于分类任务，学习到的距离函数用于测量实例与类相关原型之间的距离。对五个基准的评估证明了所提出的方法在零样本和广义零样本学习问题中优于最先进的方法。通过定义非线性映射函数来从相关语义信息中学习每个类的视觉原型，提供了一种简单的基于理论的原型学习方法。对于分类任务，学习到的距离函数用于测量实例与类相关原型之间的距离。对五个基准的评估证明了所提出的方法在零样本和广义零样本学习问题中优于最先进的方法。