Can Transformers process images?

Traditional transformers require sequences, so images are typically split into fixed-size patches (e.g., 16x16 pixels). Each patch is flattened and linearly projected into embedding vectors, analogous to word tokens in NLP. Positional embeddings are added to preserve spatial information. This sequence of patch embeddings forms the input to the transformer encoder, which uses self-attention to model relationships between patches globally across the image. While achieving state-of-the-art results, this approach often requires substantial computational resources and large datasets for training compared to some CNNs.

Vision Transformers enable efficient global feature learning, significantly advancing image classification, object detection (e.g., DETR), and segmentation tasks. They form the backbone of powerful models like ViT, DeiT, and Swin Transformers. Key implementation steps involve preprocessing images into patch tokens, adding positional data, and feeding them into the transformer layers. Their primary value lies in superior modeling of long-range dependencies across the entire image, leading to high performance in applications such as medical image analysis and autonomous driving.