Pourpanah Farhad, Abdar Moloud, Luo Yuxuan, Zhou Xinlei, Wang Ran, Lim Chee Peng, Wang Xi-Zhao, Wu Q M Jonathan
IEEE Trans Pattern Anal Mach Intell. 2023 Apr;45(4):4051-4070. doi: 10.1109/TPAMI.2022.3191696. Epub 2023 Mar 7.
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. First, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.
广义零样本学习（GZSL）旨在训练一个模型，以便在监督学习期间某些输出类别未知的情况下对数据样本进行分类。为了解决这一具有挑战性的任务，GZSL利用已见（源）类别和未见（目标）类别的语义信息来弥合已见和未见类别之间的差距。自其被提出以来，已经制定了许多GZSL模型。在这篇综述论文中，我们对GZSL进行了全面综述。首先，我们概述了GZSL，包括问题和挑战。然后，我们介绍了GZSL方法的层次分类，并讨论了每一类中的代表性方法。此外，我们还讨论了可用的基准数据集和GZSL的应用，同时讨论了研究差距和未来研究方向。
