Coarse to Fine

Coarse-to-fine approaches represent a powerful paradigm in various machine learning domains, aiming to improve efficiency and accuracy by processing information hierarchically, starting with a broad overview and progressively refining details. Current research focuses on applying this strategy across diverse tasks, including image and video processing, natural language understanding, robotics, and game theory, often employing transformer networks, diffusion models, and curriculum learning techniques to achieve this hierarchical processing. The success of these methods demonstrates their significance in enhancing model performance, particularly in complex scenarios with high dimensionality or noisy data, leading to improvements in various applications such as text-to-speech synthesis, object detection, and image inpainting.