  Most existing methods of semantic segmentation still suffer from two aspects
of challenges: intra-class inconsistency and inter-class indistinction. To
tackle these two problems, we propose a Discriminative Feature Network (DFN),
which contains two sub-networks: Smooth Network and Border Network.
Specifically, to handle the intra-class inconsistency problem, we specially
design a Smooth Network with Channel Attention Block and global average pooling
to select the more discriminative features. Furthermore, we propose a Border
Network to make the bilateral features of boundary distinguishable with deep
semantic boundary supervision. Based on our proposed DFN, we achieve
state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean
IOU on Cityscapes dataset.
