GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks
Apple researchers introduce GH-ESD, a method designed to discover error slices in instance-level vision tasks such as object detection and segmentation. Unlike existing slice discovery approaches that are effective for image-level classification, GH-ESD addresses the unique challenges of instance-level tasks by leveraging grounded hypotheses to identify systematic failures related to contextual and spatial patterns.
Why it matters: This research enables more robust evaluation of vision models by systematically uncovering failure modes in complex instance-level tasks.
Full story at: Apple Machine Learning Research ↗