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

借鉴比例整合策略和改良方法，我们提出了一种全新的名为 MGNet 的网络，它包括一个 Fine-Rescaling 和 Merging 模块（FRM）来提高提取空间关系的能力，一个 Primary Prediction Guiding 模块（PPG）来更好地从融合后的特征中挖掘剩余语义，并且我们使用一种新的带有不确定性感知损失的损失函数来监督模型生成高置信度的分割地图。与现有的必须在不同设置下训练的玻璃分割模型不同，我们的模型在统一设置下训练，并在三个流行的公共数据集上取得了卓越的性能。可在指定链接获取代码。

多尺度与主要预测指导的玻璃分割

Glass Segmentation with Multi Scales and Primary Prediction Guiding

Glass-like objects are widespread in daily life but remain intractable to be
segmented for most existing methods. The transparent property makes it
difficult to be distinguished from background, while the tiny separation
boundary further impedes the acquisition of their exact contour. In this paper,
by revealing the key co-evolution demand of semantic and boundary learning, we
propose a Selective Mutual Evolution (SME) module to enable the reciprocal
feature learning between them. Then to exploit the global shape context, we
propose a Structurally Attentive Refinement (SAR) module to conduct a
fine-grained feature refinement for those ambiguous points around the boundary.
Finally, to further utilize the multi-scale representation, we integrate the
above two modules into a cascaded structure and then introduce a Reciprocal
Feature Evolution Network (RFENet) for effective glass-like object
segmentation. Extensive experiments demonstrate that our RFENet achieves
state-of-the-art performance on three popular public datasets.

本文提出了选择性互动进化（SME）模块，并引入了结构化精细的细化（SAR）模块和反向演化的特征进化网络（RFENet），成功地实现了玻璃样的物体分割，性能优于三个公共数据集。

RFENet：面向玻璃分割的特征互进化

RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the
real world. Sensing these objects has many applications, including robot
navigation and grasping. However, this task is very challenging due to the
arbitrary scenes behind glass-like objects. This paper aims to solve the
glass-like object segmentation problem via enhanced boundary learning. In
particular, we first propose a novel refined differential module that outputs
finer boundary cues. We then introduce an edge-aware point-based graph
convolution network module to model the global shape along the boundary. We use
these two modules to design a decoder that generates accurate and clean
segmentation results, especially on the object contours. Both modules are
lightweight and effective: they can be embedded into various segmentation
models. In extensive experiments on three recent glass-like object segmentation
datasets, including Trans10k, MSD, and GDD, our approach establishes new
state-of-the-art results. We also illustrate the strong generalization
properties of our method on three generic segmentation datasets, including
Cityscapes, BDD, and COCO Stuff. Code and models is available at
https://github.com/hehao13/EBLNet.

本文提出使用增强边缘学习来解决玻璃之类物品的分割问题。通过引入新的微调差分模块和边缘感知点基图卷积网络模块，该方法能够轻松嵌入到各种分割模型中，实现较高的分割精度和清晰度