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
@article{arxiv.2203.01536,
title = {Recent Advances in Vision Transformer: A Survey and Outlook of Recent Work},
author = {Khawar Islam},
journal= {arXiv preprint arXiv:2203.01536},
year = {2023}
}
Comments
Added AAAI 2022 methods and working on ICLR 2022 methods and ICML 2022