In recent years,remarkable advancements in Artificial Intelligence(AI)across unimodal domains,such as computer vision and Natural Language Processing(NLP),have highlighted the growing importance and necessity of multimodal learning.Among the emerging techniques,the Zero-Shot Transfer(ZST)method,based on visual-language pre-trained models,has garnered widespread attention from researchers worldwide.Owing to the robust generalization capabilities of pre-trained models,leveraging visual-language pre-trained models not only enhances the accuracy of zero-shot recognition tasks but also addresses certain zero-shot downstream tasks that are beyond the scope of conventional approaches.This review provides an overview of ZST methods based on vision-language pre-trained models.First,it introduces conventional approaches to Few-Shot Learning(FSL)and summarizes its main forms.It then discusses the distinctions between ZST and FSL based on vision-language pre-trained models,highlighting the new tasks that ZST can address.Subsequently,it explores the application of ZST methods in various downstream tasks,including sample recognition,object detection,semantic segmentation,and cross-modal generation.Finally,it analyzes the challenges of current ZST methods based on vision-language pre-trained models and outlines potential future research directions.
国家科技期刊平台
NSTL
万方数据
