Research on the application of transformer in computer vision

Abstract

The Transformer is a deep neural network model that utilizes attention mechanisms to improve model performance. Initially, the Transformer gained significant attention in the field of natural language processing. In recent years, due to continuous improvements and extensions to the Transformer model structure, it has also achieved many important breakthroughs in computer vision(CV) tasks, attracting the interest of many researchers. However, there is a lack of comprehensive review articles on the application and development of the Transformer in computer vision. A summary of the Transformer’s applications and advancements in computer vision is given in this paper. It discusses the Transformer model’s fundamental ideas and organizational framework, and primarily introduces its applications in various fields such as image classification, object detection, and image generation, as well as the superiority of the Transformer+ convolutional neural network(CNN) fusion model. The paper provides a detailed analysis of classic models such as Vision Transformer(ViT), Detection Transformer(DETR) and discusses their strengths, weaknesses, and improvement methods. Finally, the paper summarizes and looks forward to the Transformer’s evolution in computer vision.