Abstract
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
Abstract (translated)
日常生活中，我们处处都可以看到玻璃般的物体，这对于现有方法来说很难对其进行分割。透明度的性质给从混乱的背景中检测它们带来了巨大的挑战，而模糊的分割边界进一步阻碍了获取其精确轮廓。忽略玻璃的移动机器具有很大的撞墙风险或镜子中反射的物体的分析困难，因此准确地定位玻璃般的物体并完全理解其轮廓具有重大的实际意义。在本文中，我们受到规模集成策略和优化方法的影响，提出了一种名为MGNet的新网络，它包括一个Fine-Rescaling和Merging模块（FRM）和一个Primary Prediction Guiding模块（PPG）。此外，我们还使用具有不确定性感知损失的模型监督方法，以产生高置信度分割图。与必须针对不同数据集在不同的设置上进行训练的现有玻璃分割模型不同，我们的模型在一致的设置下训练，已经在三个流行的公共数据集上取得了卓越的性能。代码可于以下链接获取：
URL
https://arxiv.org/abs/2402.08571
