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.
More from Research
- PIRL adds closed-loop verification to RL post-training and improves accuracy on reasoning and code tasks — This_Ad9834 · 2026-07-28
- Vstone unveils a 97 kg mobile dual-arm humanoid robot for factory and logistics work — CyberRobooo · 2026-07-28
- A 200-patient synthetic table stayed unique after removing all identifiers — MaziyarPanahi · 2026-07-28
- GitHub repo reproduces Patch Policy for embodied control with DINOv2 features — k7agar · 2026-07-28
- Simons Institute panel asks how researchers should adapt to automation — ceciletamura · 2026-07-28
- A research agent works better when it can stop instead of forcing an answer — Harshit-24 · 2026-07-28