U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation

• URL: http://arxiv.org/abs/2607.20705v1
• Date: Wed, 22 Jul 2026 20:23:00 GMT
• Title: U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation
• Abstract summary: Uncertainty-Guided Cascade Forward Refinement (U-CFR)<n>We propose a novel inference-time framework that enables models to autonomously self-correct after each user interaction.<n>It reduces the required clicks by over 10% on challenging datasets like Berkeley.
• Score: 2.3488056916440856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 input. To support this process, we design a dual-head network with a shared encoder-decoder backbone: a segmentation head ensures region consistency, while an edge head sharpens boundary alignment. In inference, U-CFR launches a cascade of refinement steps, where each stage leverages the uncertainty-driven pseudo-clicks to refine the mask progressively. Experiments on standard benchmark datasets demonstrate that the proposed U-CFR improves click efficiency, initial mask quality, and boundary accuracy. It reduces the required clicks by over 10% on challenging datasets like Berkeley and offers a more intelligent and efficient interactive annotation.

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