Multimodal learning provides a path to fully utilize all types of information related to the modeling target to provide the model with a global vision. Zero-shot learning (ZSL) is a general solution for incorporating prior knowledge into data-driven models and achieving accurate class identification. The combination of the two, known as multimodal ZSL (MZSL), can fully exploit the advantages of both technologies and is expected to produce models with greater generalization ability. However, the MZSL algorithms and applications have not yet been thoroughly investigated and summarized. This study fills this gap by providing an objective overview of MZSL's definition, typical algorithms, representative applications, and critical issues. This article will not only provide researchers in this field with a comprehensive perspective, but it will also highlight several promising research directions.

中文翻译：

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多模态零样本学习综述

多模态学习提供了一条路径，可以充分利用与建模目标相关的所有类型的信息，为模型提供全局视野。零样本学习 (ZSL) 是将先验知识融入数据驱动模型并实现准确类别识别的通用解决方案。两者的结合，被称为多模态 ZSL（MZSL），可以充分发挥这两种技术的优势，有望产生具有更强泛化能力的模型。然而，MZSL 算法和应用尚未得到彻底研究和总结。本研究通过客观概述 MZSL 的定义、典型算法、代表性应用和关键问题来填补这一空白。本文不仅将为该领域的研究人员提供一个全面的视角，