GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks

Apple · 2 months ago · 25

Researchers developed GH-ESD, a framework for discovering error slices in vision models that uses language model priors and vision-language models to identify systematic failures in instance-level tasks like object detection and segmentation. The method achieved Precision@10 of 0.73 versus 0.63 for baseline approaches on a new GESD benchmark for detection tasks. This approach enables identification of interpretable, spatially grounded failure patterns that can guide targeted model improvements beyond existing attribute-based slice discovery methods.