细粒度图像分类上Vision Transformer的发展综述OA北大核心CSTPCD

Fine-grained image classification(FGIC)has always been an important problem in computer vision.Compared to traditional image classification tasks,FGIC faces the challenge of extremely similar inter-class objects,which further increases the difficulty of the task.With the development of deep learning,Vision Transformer(ViT)models have become popular in the field of vision and have been introduced into FGIC tasks.This paper introduces the challenges faced by FGIC tasks,provides an overview of the ViT model,and analyzes its characteristics.The comprehensive review is primarily based on the model structure and covers FGIC algorithms based on ViT.It includes feature extraction,feature relation modeling,feature attention,and feature enhancement as the main aspects.Each algorithm is summarized,and its advantages and disadvantages are analyzed.Following that,a comparison of the performance of different ViT models on the same public dataset is conducted to validate their effectiveness in the FGIC tasks.Furthermore,the limitations of current research are pointed out,and future research directions are proposed to further explore the potential of ViT in FGIC.