Image Transformer

Image transformers leverage the power of self-attention mechanisms, initially developed for natural language processing, to analyze and manipulate images and videos. Current research focuses on improving efficiency (e.g., through techniques like group-shifted window attention and wavelet transforms), expanding applications (including image restoration, inpainting, generation, and video understanding), and addressing challenges like memory consumption and bias in model outputs. This rapidly evolving field is significantly impacting computer vision, enabling advancements in diverse areas such as medical image analysis, robotic interaction, and creative content generation.