Position Embeddings for Vision Transformers, Explained

Vision Transformers Explained Series

The Math and the Code Behind Position Embeddings in Vision Transformers

Since their introduction in 2017 with Attention is All You Need¹, transformers have established themselves as the state of the art for natural language processing (NLP). In 2021, An Image is Worth 16x16 Words² successfully adapted transformers for computer vision tasks. Since then, numerous transformer-based architectures have been proposed for computer vision.

This article examines why position embeddings are a necessary component of …

attention computer vision machine learning transformers vision-transformer