Abstract. We notice information flow in convolutional neural networks
is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of
complex scenes. In this paper, we propose the point-wise spatial attention network (PSANet) to relax the local neighborhood constraint. Each
position on the feature map is connected to all the other ones through
a self-adaptively learned attention mask. Moreover, information propagation in bi-direction for scene parsing is enabled. Information at other
positions can be collected to help the prediction of the current position
and vice versa, information at the current position can be distributed
to assist the prediction of other ones. Our proposed approach achieves
top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its
effectiveness and generality.