  Recent development in fully convolutional neural network enables efficient
end-to-end learning of semantic segmentation. Traditionally, the convolutional
classifiers are taught to learn the representative semantic features of labeled
semantic objects. In this work, we propose a reverse attention network (RAN)
architecture that trains the network to capture the opposite concept (i.e.,
what are not associated with a target class) as well. The RAN is a three-branch
network that performs the direct, reverse and reverse-attention learning
processes simultaneously. Extensive experiments are conducted to show the
effectiveness of the RAN in semantic segmentation. Being built upon the
DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%)
for the challenging PASCAL-Context dataset. Significant performance
improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and
ADE20K datasets.
