Autoregressive Image

Autoregressive image modeling leverages sequential processing to generate or reconstruct images, inspired by the success of autoregressive language models. Current research focuses on improving the efficiency and scalability of these models, exploring architectures like diffusion models and employing techniques such as conditional generation and stochastic permutation to enhance image quality and reduce computational demands across diverse applications. This approach holds significant promise for various fields, including medical imaging (e.g., MRI reconstruction and synthesis), remote sensing (e.g., wildfire prediction), and large-scale image generation, by enabling high-quality image generation from limited data and offering improved control over the generation process.