Vision Transformers: From Theory to Implementation

Abstract

The field of computer vision has passed through a paradigm shift with the appearance of Vision Transformers (ViTs), an revolutionary technique inspired by natural language processing (NLP). ViTs represents a paradigm shift, transferring far from traditional convolutional neural networks (CNNs) by the usage of the strength of self-attention mechanisms for comprehensive image understanding. This paper offers a comprehensive explanation of ViTs, starting with an exploration of their theoretical foundations and concepts, along with the mechanisms of self-attention and multi-headed attention, to their implementation. It presents practical insights for researchers and practitioners. Furthermore, we highlighted the actual-world applications of ViTs, which cover a number of domains from healthcare to autonomous systems, in order to reveal their adaptability and potential.