Apple ML Research proposes GH-ESD for grounded error-slice discovery in vision tasks
Apple ML Research · rss · 2026-07-27
Apple ML Research introduces GH-ESD (Grounded Hypothesis-Driven Error Slice Discovery), a new method for finding systematic failure slices in instance-level vision tasks such as detection and segmentation.
- The paper argues that existing slice discovery methods work reasonably for image-level classification, but fall short when failures are driven by contextual, relational, and spatially grounded patterns.
- GH-ESD is designed to discover error slices from those grounded visual patterns instead of treating slices only as clusters in representation space or as combinations of predefined attributes.
- The goal is to make robustness analysis and evaluation more effective for tasks where object interactions and local context matter.
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