  As a special type of transformer, Vision Transformers (ViTs) are used to
various computer vision applications (CV), such as image recognition. There are
several potential problems with convolutional neural networks (CNNs) that can
be solved with ViTs. For image coding tasks like compression, super-resolution,
segmentation, and denoising, different variants of the ViTs are used. The
purpose of this survey is to present the first application of ViTs in CV. The
survey is the first of its kind on ViTs for CVs to the best of our knowledge.
In the first step, we classify different CV applications where ViTs are
applicable. CV applications include image classification, object detection,
image segmentation, image compression, image super-resolution, image denoising,
and anomaly detection. Our next step is to review the state-of-the-art in each
category and list the available models. Following that, we present a detailed
analysis and comparison of each model and list its pros and cons. After that,
we present our insights and lessons learned for each category. Moreover, we
discuss several open research challenges and future research directions.
