Glass Segmentation with Multi Scales and Primary Prediction Guiding

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 con tours. 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 integra tion 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 Pre diction 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. Un like the existing glass segmentation models that must be trained on different set tings with respect to varied datasets, our model are trained under consistent set tings and has achieved superior performance on three popular public datasets.