Image Modeling

Image modeling aims to learn efficient representations of images, enabling tasks like image generation, recognition, and manipulation. Current research focuses on self-supervised learning techniques, particularly masked image modeling (MIM), which trains models to reconstruct missing image parts, and on improving the interpretability and robustness of these models through methods like generalized integrated gradients. These advancements are significant because they improve the efficiency and effectiveness of computer vision systems, leading to better performance in various applications and a deeper understanding of how these models function.