Abstract
Current approaches to dichotomous image segmentation (DIS) treat image matting and object segmentation as fundamentally different tasks. As improvements in image segmentation become increasingly challenging to achieve, combining image matting and grayscale segmentation techniques offers promising new directions for architectural innovation. Inspired by the possibility of aligning these two model tasks, we propose a new architectural approach for DIS called Confidence-Guided Matting (CGM). We created the first CGM model called Background Erase Network (BEN). BEN is comprised of two components: BEN Base for initial segmentation and BEN Refiner for confidence refinement. Our approach achieves substantial improvements over current state-of-the-art methods on the DIS5K validation dataset, demonstrating that matting-based refinement can significantly enhance segmentation quality. This work opens new possibilities for cross-pollination between matting and segmentation techniques in computer vision.
Abstract (translated)
目前的二值图像分割（DIS）方法将图像抠图和对象分割视为本质上不同的任务。随着图像分割改进变得越来越具有挑战性，结合图像抠图和灰度分割技术为架构创新提供了有前景的新方向。受这两种模型任务相协调的可能性启发，我们提出了一种新的针对DIS的架构方法，称为基于置信度引导抠图（CGM）。我们创建了第一个CGM模型——背景擦除网络（BEN）。BEN由两个组成部分构成：用于初始分割的BEN Base和用于置信度优化的BEN Refiner。我们的方法在DIS5K验证数据集上比当前最先进的方法实现了显著改进，表明基于抠图的优化可以大幅提升分割质量。这项工作开启了计算机视觉领域中图像抠图与分割技术相互融合的新可能。
URL
https://arxiv.org/abs/2501.06230
