  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
