By combining foundation models with diverse training data, you can build correspondence matching systems that work across both classical vision tasks and modern generative applications like image editing.
FreeMatching is a framework for matching visual correspondence across images that goes beyond traditional motion and geometry assumptions. It combines generative and semantic models trained on diverse data sources to handle challenging transformations in image editing and generation tasks, while maintaining competitive performance on standard benchmarks.