Transformer在计算机视觉场景下的研究综述

doi:10.11896/jsjkx.221100076

Abstract

Abstract: Transformer is an attention-based encoder-decoder architecture.Due to its long-range sequence modeling and parallel computing capability,Transformer have made a significant breakthrough in natural language processing and is gradually expanding to computer vision(CV) fields,which has become an important research direction in CV tasks.Three sorts of visual Transformer-based CV task,including classification,object detection and segmentation,are focused on by this paper,which summarizes their application and modification.Starting from image classification,this paper first analyses the existing issue in vision Transformer including data size,structure and computational efficiency,then sorts out the corresponding solutions according to the issue.Besides,this paper provides a literature review on object detection and segmentation,which organizes these methods accor-ding to their structures and motivations and summarizes their corresponding pros and cons.Finally,the challenges and future development trends of the Transformer in vision transformer are summarized and discussed in this paper.

Key words: Vison Transformer, Computer vision, Image classification, Object detection, Image segmentation

CLC Number:

TP391

CHEN Luoxuan, LIN Chengchuang, ZHENG Zhaoliang, MO Zefeng, HUANG Xinyi, ZHAO Gansen. Review of Transformer in Computer Vision[J].Computer Science, 2023, 50(12): 130-147.

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