面向图像分类的Vision Transformer研究综述OA北大核心CSTPCD

ViT as a model based on the Transformer architecture has shown good results in image classification tasks.In this study,the application of ViT on image classification tasks was systematically summarized.Firstly,the functional characteristics of the ViT framework and its four modules(patch module,position encoding,multihead attention mechanism and feed-forward neural network)were briefly introduced.Secondly,the application of ViT in image classification tasks was summarized with the improvement measures of the four modules.Due to the fact that different model structures and improvement measures could have a significant impact on the final classification per-formance,a side-by-side comparison of various types of ViTs was made in this paper.Finally,the advantages and limitations of ViT in image classification were pointed out,and possible future research directions were proposed to break the limitations,and further to extend the application of ViT in other computer vision tasks.The extension of ViT to a wider range of computer vision fields,such as video understanding,was explored.