发表机构
University of Idaho; Idaho National Laboratory（爱达荷大学; 爱达荷国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对交互式图像分割中现有方法的不足，提出U-CFR框架，通过引入边界感知不确定性分数引导伪点击，利用双头网络和级联细化步骤，提升了点击效率、掩码质量和边界精度，实现更智能高效的交互式标注。
AI 中文摘要
交互式图像分割对高效图像标注至关重要，但现有方法往往需要多次校正点击或依赖收敛缓慢的被动细化方案。我们提出了不确定性引导级联前向细化（U-CFR），这是一种新颖的推理时框架，能使模型在每次用户交互后自主自我校正。U-CFR引入边界感知不确定性分数，融合分割不确定性、轮廓梯度和显式边缘预测来引导内部伪点击的放置。设计了具有共享编码器-解码器主干的双头网络，在推理时进行一系列细化步骤。实验表明U-CFR提高了点击效率、初始掩码质量和边界精度，减少了所需点击次数，提供了更智能高效的交互式标注。
英文摘要
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.
Comments12 pages, 3 figures, 4 tables, ICPR 2026
