A Survey on Vision Transformer.

Area of Science:

• Computer Vision
• Deep Learning
• Artificial Intelligence

Background:

• Transformers, initially developed for natural language processing, are deep neural networks utilizing self-attention mechanisms.
• Their strong representational power is driving exploration into computer vision applications.

Purpose of the Study:

• To provide a comprehensive review of vision transformer models.
• To categorize these models based on their application in different computer vision tasks.
• To analyze the advantages and disadvantages of various vision transformer approaches.

Main Methods:

• Categorization of vision transformers into backbone networks, high/mid-level vision, low-level vision, and video processing.
• Analysis of efficient transformer methods for real-device applications.
• Brief examination of the self-attention mechanism's role in computer vision.

Main Results:

• Transformer-based models demonstrate performance comparable to or exceeding convolutional and recurrent neural networks on visual benchmarks.
• Vision transformers require less vision-specific inductive bias, contributing to their growing popularity.
• The review covers diverse applications, including efficient methods for practical deployment.

Conclusions:

• Vision transformers represent a significant advancement in computer vision.
• The paper highlights their potential and discusses current challenges and future research directions.
• Further investigation into efficient and specialized vision transformer architectures is warranted.