Enhancing Unknown Object Instance Segmentation through Error-Informed Refinement

INSTA-BEER, a fast and accurate model-agnostic refinement method, enhances unknown object instance segmentation performance by first predicting pixel-wise quad-metric boundary errors and then refining the segmentation guided by these error estimates.