# A survey of the Vision Transformers and their CNN-Transformer based Variants

**Authors:** Asifullah Khan, Zunaira Rauf, Anabia Sohail, Abdul Rehman, Hifsa Asif, Aqsa Asif, Umair Farooq  
**arXiv:** [2305.09880](https://arxiv.org/abs/2305.09880)  
**Date:** 2023-05-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.09880v4/

## Key points

- Vision transformers offer the capacity to capture global relationships within images, presenting significant learning capabilities.
- Pure vision transformers may overlook local correlations in images, affecting generalization performance.
- Hybrid vision transformers, also known as CNN-Transformer architectures, combine convolution operations and self-attention mechanisms to leverage both local and global image representations for improved performance in vision tasks.
- The survey categorizes recent vision transformer architectures with a focus on hybrid variants, discussing key features like attention mechanisms, positional embeddings, multi-scale processing, and integration of convolution.
- The study highlights the success of hybrid architectures in various computer vision applications and emphasizes their potential for exceptional performance across diverse tasks.
- This research provides valuable insights into the design principles behind hybrid vision transformers and their implications for advancing computer vision technologies.

## Summary

In their comprehensive survey titled "A survey of the Vision Transformers and their CNN-Transformer based Variants," authors Asifullah Khan, Zunaira Rauf, Anabia Sohail, Abdul Rehman, Hifsa Asif, Aqsa Asif, and Umair Farooq delve into the evolving landscape of vision transformers and hybrid architectures in computer vision applications. <br>
Vision transformers have gained traction as a potential alternative to convolutional neural networks (CNNs) due to their capacity to capture global relationships within images, offering significant learning capabilities. However, a limitation of pure vision transformers is their tendency to overlook local correlations in images, potentially impacting generalization performance. To address this limitation, recent advancements have seen the emergence of hybrid vision transformers that combine elements of both convolution operations and self-attention mechanisms. These hybrid models, also known as CNN-Transformer architectures, aim to leverage both local and global image representations for enhanced performance in various vision tasks. The authors highlight the remarkable results achieved by these hybrid architectures across different computer vision applications.<br>
The survey provides a taxonomy of recent vision transformer architectures with a specific focus on hybrid variants. Key features such as attention mechanisms, positional embeddings, multi-scale processing, and the integration of convolution are thoroughly discussed to offer insights into the design principles behind these models. Unlike previous surveys that primarily focused on individual transformer architectures or CNNs in isolation,<br> 
this study uniquely emphasizes the emerging trend of hybrid vision transformers.<br>
By showcasing the potential of hybrid vision transformers to deliver exceptional performance across diverse computer vision tasks,<br> 
this survey sheds light on the future directions and possibilities within this rapidly evolving architectural domain.<br>
The work serves as a valuable resource for researchers and practitioners seeking a deeper understanding of hybrid vision transformer models and their implications for advancing computer vision technologies.