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1. A Survey on Vision Transformer
Abstract
Transformer, first applied to the field of natural language processing, is a type
of deep neural network mainly based on the self
to its strong representation capabilities, researchers
apply transformer to computer vision tasks. In a variety of visual benchmarks,
transformer-based models perform similar to or better than other types of
networks such as convolutional and recurrent neural networks. Given its high
performance and less need for vision
receiving more and more attention from the computer vision community. In
this paper, we review these vision transformer models by categorizing them in
different tasks and analyzing
categories we explore include the backbone network, high/mid
low-level vision, and video processing. We also include efficient transformer
methods for pushing transformer into real device
Furthermore, we also take a brief look at the self
computer vision, as it is the base component in transformer. Toward the end
A Survey on Vision Transformer
Transformer, first applied to the field of natural language processing, is a type
of deep neural network mainly based on the self-attention mechanism. Thanks
to its strong representation capabilities, researchers are looking at ways to
apply transformer to computer vision tasks. In a variety of visual benchmarks,
based models perform similar to or better than other types of
networks such as convolutional and recurrent neural networks. Given its high
rformance and less need for vision-specific inductive bias, transformer is
receiving more and more attention from the computer vision community. In
this paper, we review these vision transformer models by categorizing them in
different tasks and analyzing their advantages and disadvantages. The main
categories we explore include the backbone network, high/mid
level vision, and video processing. We also include efficient transformer
methods for pushing transformer into real device-based app
Furthermore, we also take a brief look at the self-attention mechanism in
computer vision, as it is the base component in transformer. Toward the end
Transformer, first applied to the field of natural language processing, is a type
attention mechanism. Thanks
are looking at ways to
apply transformer to computer vision tasks. In a variety of visual benchmarks,
based models perform similar to or better than other types of
networks such as convolutional and recurrent neural networks. Given its high
specific inductive bias, transformer is
receiving more and more attention from the computer vision community. In
this paper, we review these vision transformer models by categorizing them in
their advantages and disadvantages. The main
categories we explore include the backbone network, high/mid-level vision,
level vision, and video processing. We also include efficient transformer
based applications.
attention mechanism in
computer vision, as it is the base component in transformer. Toward the end

2. of this paper, we discuss the challenges and provide several further research
directions for vision transformers.