Introduction to Vision Transformers (ViT)

Vision Transformers (ViTs) are transformative models that bridge the worlds of image analysis and self-attention-based architectures. They combine the innovative architecture of Transformers, originally designed for sequential data, to process images by splitting them into patches, flattening those patches, and then applying a Transformer architecture to learn complex patterns and relationships within images. Unlike traditional Convolutional Neural Networks (CNNs), ViTs rely on self-attention mechanisms, enabling them to capture long-range dependencies and global context within images. They have applications in various real-world tasks, including image classification, object detection, image segmentation, action recognition, generative modeling, and multi-modal tasks. Vision Transformers' ability to leverage pre-trained models for transfer learning also significantly reduces the need for extensive labeled data, making them practical for a wide range of applications.

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