  The recent studies on semantic segmentation are starting to notice the
significance of the boundary information, where most approaches see boundaries
as the supplement of semantic details. However, simply combing boundaries and
the mainstream features cannot ensure a holistic improvement of semantics
modeling. In contrast to the previous studies, we exploit boundary as a
significant guidance for context aggregation to promote the overall semantic
understanding of an image. To this end, we propose a Boundary guided Context
Aggregation Network (BCANet), where a Multi-Scale Boundary extractor (MSB)
borrowing the backbone features at multiple scales is specifically designed for
accurate boundary detection. Based on which, a Boundary guided Context
Aggregation module (BCA) improved from Non-local network is further proposed to
capture long-range dependencies between the pixels in the boundary regions and
the ones inside the objects. By aggregating the context information along the
boundaries, the inner pixels of the same category achieve mutual gains and
therefore the intra-class consistency is enhanced. We conduct extensive
experiments on the Cityscapes and ADE20K databases, and comparable results are
achieved with the state-of-the-art methods, clearly demonstrating the
effectiveness of the proposed one.
