  As the superiority of context information gradually manifests in advanced
semantic segmentation, learning to capture the compact context relationship can
help to understand the complex scenes. In contrast to some previous works
utilizing the multi-scale context fusion, we propose a novel module, named
Dense Context-Aware (DCA) module, to adaptively integrate local detail
information with global dependencies. Driven by the contextual relationship,
the DCA module can better achieve the aggregation of context information to
generate more powerful features. Furthermore, we deliberately design two
extended structures based on the DCA modules to further capture the long-range
contextual dependency information. By combining the DCA modules in cascade or
parallel, our networks use a progressive strategy to improve multi-scale
feature representations for robust segmentation. We empirically demonstrate the
promising performance of our approach (DCANet) with extensive experiments on
three challenging datasets, including PASCAL VOC 2012, Cityscapes, and ADE20K.
