  In this work, we address the challenging issue of scene segmentation. To
increase the feature similarity of the same object while keeping the feature
discrimination of different objects, we explore to propagate information
throughout the image under the control of objects' boundaries. To this end, we
first propose to learn the boundary as an additional semantic class to enable
the network to be aware of the boundary layout. Then, we propose unidirectional
acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs),
which structurize the image via building graphic pixel-by-pixel connections, in
an efficient and effective way. Furthermore, we propose a boundary-aware
feature propagation (BFP) module to harvest and propagate the local features
within their regions isolated by the learned boundaries in the UAG-structured
image. The proposed BFP is capable of splitting the feature propagation into a
set of semantic groups via building strong connections among the same segment
region but weak connections between different segment regions. Without bells
and whistles, our approach achieves new state-of-the-art segmentation
performance on three challenging semantic segmentation datasets, i.e.,
PASCAL-Context, CamVid, and Cityscapes.
