GH-ESD is a novel framework for discovering systematic failure modes in instance-level vision tasks. The approach addresses the limitations of representation-space clustering by focusing on contextual relational and spatially grounded visual patterns that cause errors in object detection and segmentation. By automating error slice discovery, the method gives developers deeper diagnostic insights into model robustness beyond aggregate accuracy metrics.
Opening Kapyn…