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

RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation