  Transformer, an attention-based encoder-decoder architecture, has not only
revolutionized the field of natural language processing (NLP), but has also
done some pioneering work in the field of computer vision (CV). Compared to
convolutional neural networks (CNNs), the Vision Transformer (ViT) relies on
excellent modeling capabilities to achieve very good performance on several
benchmarks such as ImageNet, COCO, and ADE20k. ViT is inspired by the
self-attention mechanism in natural language processing, where word embeddings
are replaced with patch embeddings.
  This paper reviews the derivatives of ViT and the cross-applications of ViT
with other fields.
