U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation
Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly. We propose Uncertainty-Guided Cascade Forward Refinement (U-CFR), a novel inference-time framework that enables models to autonomously self-correct after each user interaction. U-CFR introduces a boundary-aware uncertainty score that fuses segmentation uncertainty, contour gradients, and explicit edge predictions to guide the placement of internal pseudo-clicks. These self-generated clicks target the most ambiguous boundary regions, providing strong corrective signals without additional manual ...
| Authors | Elijah Danquah Darko, Min Xian, Terence Soule et al. |
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| Primary category | cs.CV |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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