An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
This work addresses image classification for computer vision researchers by introducing a novel, efficient alternative to CNNs, though it builds on existing transformer paradigms.
The paper tackled image recognition by applying a pure transformer directly to image patches, achieving excellent results on benchmarks like ImageNet with fewer computational resources than state-of-the-art convolutional networks.
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.