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Transformer design is the de facto standard for natural language processingtasks. The success of the transformer design in natural language processing haslately piqued the interest of researchers in the domain of computer vision.When compared to Convolutional Neural Networks (CNNs), Vision Transformers(ViTs) are becoming more popular and dominant solutions for many visionproblems. Transformer-based models outperform other types of networks, such asconvolutional and recurrent neural networks, in a range of visual benchmarks.We evaluate various vision transformer models in this work by dividing theminto distinct jobs and examining their benefits and drawbacks. ViTs canovercome several possible difficulties with convolutional neural networks(CNNs). The goal of this survey is to show the first use of ViTs in CV. In thefirst phase, we categorize various CV applications where ViTs are appropriate.Image classification, object identification, image segmentation, videotransformer, image denoising, and NAS are all CV applications. Our next stepwill be to analyze the state-of-the-art in each area and identify the modelsthat are currently available. In addition, we outline numerous open researchdifficulties as well as prospective research possibilities.

ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain