How Vision Transformers Replaced CNNs by Treating Images as Patch Sequences
Vision Transformers (ViT) adapt the Transformer architecture — originally designed for text — to process images by dividing them into fixed-size patches treated as sequential tokens. This approach was made possible after Transformers revolutionized NLP following Google's 2017 'Attention Is All You Need' paper, which introduced self-attention mechanisms. For decades, Convolutional Neural Networks (CNNs) dominated computer vision by using learnable filters to detect local patterns hierarchically, but they struggled with long-range dependencies and rigid geometric processing. ViT addresses these limitations by allowing every image patch to attend to every other patch, enabling richer global context understanding. A standard ViT splits a 224x224 image into 196 non-overlapping 16x16 patches, each flattened and projected into a token vector before being fed into the Transformer.
This is an AI-generated summary. ShortSingh links to the original source for the complete article.
Discussion (0)
Log in to join the discussion and vote.
Log in