A decadal survey of zero-shot image classification

Abstract

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.