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.

本文提出了逆向注意力网络（RAN），通过同时进行直接、反向和逆向 - 注意力学习过程，使得全卷积神经网络不仅可以学习标签化的语义对象的代表性语义特征，还可以学习与目标类别不相关的特征，在对 Pascal-Context 数据集和其他数据集进行实验后，在 DeepLabv2-LargeFOV 基础上建立的 RAN 在语义分割领域达到了最先进的 mIoU 得分（48.1％），同时也显著提高了 PASCAL-VOC、Person-Part、NYUDv2 和 ADE20K 数据集的性能。