The original title is "Vision Transformers — How Transformers Learned to See (Part 2 of 3)"

Short summary

Part 2 explores how Vision Transformers (ViT) brought Transformer architecture to computer vision. By dividing images into 16×16 patches and treating each as a token, ViTs overcome CNNs' limited receptive field and rigid geometric structure, enabling direct attention between distant image regions. Google's 2020 breakthrough uses learnable patch embeddings, a [CLS] aggregation token, and positional encodings within a standard Transformer encoder.

• •ViT divides images into patches and processes them like tokens in an NLP Transformer
• •Direct attention between distant patches overcomes CNNs' limited receptive field and scaling bottlenecks
• •Architecture uses learnable [CLS] token for aggregation and positional embeddings to encode 2D spatial structure

Generated with AI, which can make mistakes.