Point-based interactive image segmentation can ease the burden of mask annotation in applications such as semantic segmentation and image editing. However, fully extracting the target mask with limited user inputs remains challenging. We introduce a novel method, Variance-Insensitive and Target-Preserving Mask Refinement to enhance segmentation quality with fewer user inputs. Regarding the last segmentation result as the initial mask, an iterative refinement process is commonly employed to continually enhance the initial mask. Nevertheless, conventional techniques suffer from sensitivity to the variance in the initial mask. To circumvent this problem, our proposed method incorporates a mask matching algorithm for ensuring consistent inferences from different types of initial masks. We also introduce a target-aware zooming algorithm to preserve object information during downsampling, balancing efficiency and accuracy. Experiments on GrabCut, Berkeley, SBD, and DAVIS datasets demonstrate our method's state-of-the-art performance in interactive image segmentation.

翻译：基于点的交互式图像分割可减轻语义分割和图像编辑等应用中掩码标注的负担。然而，在有限用户输入条件下完整提取目标掩码仍具挑战性。我们提出一种新颖方法——方差不敏感与目标保持的掩码精炼，通过更少的用户输入提升分割质量。将上次分割结果视为初始掩码，通常采用迭代精炼过程持续优化初始掩码。然而，传统技术对初始掩码的方差敏感。为解决该问题，本方法引入掩码匹配算法确保不同类型初始掩码推理结果的一致性。同时提出目标感知缩放算法，在下采样过程中保持目标信息，平衡效率与精度。在GrabCut、Berkeley、SBD和DAVIS数据集上的实验表明，本方法在交互式图像分割中达到了当前最优性能。