零样本图像分类综述:十年进展

A decadal survey of zero-shot image classification

零样本图像分类指训练集和测试集在数据的类别上没有交集的情况下进行图像分类.该技术是解决类别标签缺失问题的一种有效手段,因此受到了日益广泛的关注.自提出此问题至今,零样本图像分类的研究已经大致有十年时间.本文系统地对过去十年中零样本图像分类技术的研究进展进行了综述,主要包括以下4个方面.首先介绍零样本图像分类技术的研究意义及其应用价值,然后重点总结和归纳零样本图像分类的发展过程和研究现状,接下来介绍常用的数据集和评价准则,以及与零样本学习相关的技术的区别和联系,最后分析有待深入研究的热点与难点问题,并对未来的发展趋势进行了展望.

Zero-shot image classification refers to learning a visual classifier for categories with zero training examples.This method can effectively solve problems in which the labeled data for some classes are absent and has therefore gained a considerable attention recently.It has been approximately a decade since this technology was first developed.This paper systematically summarizes the research progress over the past decade in this field.First,we introduce the significance and practical application value of zero-shot image classification.Next,the research processes and typical approaches are summarized in detail.Further,we comprehensively review existing datasets and evaluation metrics,together with the relation between zero-shot image classification and other related techniques.Finally,we analyze the hot spots and existing challenges that need to be further studied and emphasize the future trends in this research area.