  In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to
learn long-range contextual features for semantic segmentation. Rather than
directly construct the graph based on the backbone features, our BGR module
explores a reasonable way to combine segmentation erroneous regions with the
graph construction scenario. Motivated by the fact that most hard-to-segment
pixels broadly distribute on boundary regions, our BGR module uses the boundary
score map as prior knowledge to intensify the graph node connections and
thereby guide the graph reasoning focus on boundary regions. In addition, we
employ an efficient graph convolution implementation to reduce the
computational cost, which benefits the integration of our BGR module into
current segmentation backbones. Extensive experiments on three challenging
segmentation benchmarks demonstrate the effectiveness of our proposed BGR
module for semantic segmentation.
