基于多尺度与初始预测引导的玻璃分割
Glass Segmentation with Multi Scales and Primary Prediction Guiding
AI总结：
针对类玻璃物体透明且边界模糊导致分割困难的问题，提出MGNet网络，利用FRM模块提取空间关系并借助PPG模块挖掘融合特征语义，结合不确定性损失函数，在一致设置下于三个公开数据集取得卓越性能。
摘要：
类玻璃物体在日常生活中随处可见，现有方法很难对其进行分割。透明特性给从混乱背景中检测它们带来了巨大挑战，而模糊的分离边界进一步阻碍了获取其精确轮廓。忽视玻璃的移动机器面临撞上透明屏障或分析镜中反射物体的困难风险，因此准确定位类玻璃物体并完全弄清其轮廓具有重要意义。本文受尺度整合策略和细化方法启发，提出了一种名为 MGNet 的新网络，它包含一个 Fine-Rescaling and Merging 模块（FRM）以提升提取空间关系的能力，以及一个 Primary Prediction Guiding 模块（PPG）以更好地从融合特征中挖掘遗留语义。此外，我们使用带有 uncertainty-aware loss 的新型损失函数监督模型，以生成高置信度分割图。与必须针对不同数据集在不同设置下训练的现有玻璃分割模型不同，我们的模型在一致设置下训练，并在三个流行的公开数据集上取得了卓越性能。代码可在
英文摘要：
Glass-like objects can be seen everywhere in our daily life which are very hard for existing methods to segment them. The properties of transparencies pose great challenges of detecting them from the chaotic background and the vague separation boundaries further impede the acquisition of their exact contours. Moving machines which ignore glasses have great risks of crashing into transparent barriers or difficulties in analysing objects reflected in the mirror, thus it is of substantial significance to accurately locate glass-like objects and completely figure out their contours. In this paper, inspired by the scale integration strategy and the refinement method, we proposed a brand-new network, named as MGNet, which consists of a Fine-Rescaling and Merging module (FRM) to improve the ability to extract spatially relationship and a Primary Prediction Guiding module (PPG) to better mine the leftover semantics from the fused features. Moreover, we supervise the model with a novel loss function with the uncertainty-aware loss to produce high-confidence segmentation maps. Unlike the existing glass segmentation models that must be trained on different settings with respect to varied datasets, our model are trained under consistent settings and has achieved superior performance on three popular public datasets. Code is available at
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
