An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers

Transformer architectures are revolutionizing computer vision, a field traditionally dominated by Convolutional Neural Networks (CNNs). The guide explores state-of-the-art vision and multimodal models like ViT, DETR, BLIP, and ViLT for tasks such as image classification, segmentation, and visual question answering. It explains how vision transformers adapt the self-attention mechanism by breaking images into patches, creating embeddings, and processing them through an encoder. The content also compares these models to CNNs, noting differences in inductive bias, and to LLMs, highlighting the additional layers needed to convert image data into numerical embeddings.