  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.
