CoarseFine

Coarsefine is a term used to describe a class of methods that combine coarse (low-resolution or high-level) representations with fine (high-resolution or detailed) processing within a single workflow. It is not a single algorithm but a design principle that can be instantiated in multiple domains. The goal is to leverage the efficiency of coarse processing to guide or constrain subsequent fine processing, improving speed without sacrificing accuracy.

In a typical coarse-to-fine or coarsefine approach, an initial stage creates a rough estimate or a reduced search space. A later stage refines the result, resolving fine details. The process can be sequential, with multiple refinement steps, or iterative, where fine and coarse updates alternate.

Coarsefine concepts appear in computer vision (for example, coarse localization followed by fine-grained object recognition), image processing (multi-scale analysis and super-resolution), geographic information systems, and numerical simulations (mesh refinement). In machine learning, multi-scale features and progressive training regimes are related implementations that embody the same principle.

Advantages include reduced computation, faster inference, and better handling of large state spaces. Potential drawbacks involve error introduced at the coarse stage propagating to the fine stage, requiring careful design and validation, as well as choosing appropriate resolutions and refinement schedules.

Related concepts include coarse-to-fine, multi-resolution analysis, and hierarchical modeling.