  Vision Transformers (ViTs) are becoming more popular and dominating technique
for various vision tasks, compare to Convolutional Neural Networks (CNNs). As a
demanding technique in computer vision, ViTs have been successfully solved
various vision problems while focusing on long-range relationships. In this
paper, we begin by introducing the fundamental concepts and background of the
self-attention mechanism. Next, we provide a comprehensive overview of recent
top-performing ViT methods describing in terms of strength and weakness,
computational cost as well as training and testing dataset. We thoroughly
compare the performance of various ViT algorithms and most representative CNN
methods on popular benchmark datasets. Finally, we explore some limitations
with insightful observations and provide further research direction. The
project page along with the collections of papers are available at
https://github.com/khawar512/ViT-Survey
