Most　of　the　current　semantic　segmentation　approaches　have　achieved　state-of-the-art　performance　relying　on　fully　convolutional　networks.　However,　the　consecutive　operations　such　as　pooling　or　convolution　striding　lead　to　spatially　disjointed　object　boundaries.　We　present　a　dense　boundary　regression　architecture　(DBRS2),　which　aims　to　use　boundary　cues　to　aid　high-level　semantic　segmentation　task.　Specifically,　we　first　propose　a　multilevel　guided　low-level　boundary　(MG-LB)　learning　method,　where　we　exploit　multilevel　convolutional　features　as　guidance　for　low-level　boundary　detection.　The　predicted　MG-LB　boundaries　are　used　to　enable　consistent　spatial　grouping　and　enhance　precise　adherence　to　segment　boundaries.　Then,　we　present　a　significant　global　energy　model　based　on　boundary　penalty　and　appearance　penalty,　which　are　respectively　defined　on　the　predicted　boundaries　and　coarse　segmentations　obtained　by　the　DeepLabv3　network.　Finally,　the　refined　segmentations　are　regressed　by　minimizing　the　global　energy　model.　Extensive　experiments　over　PASCAL　VOC　2012,　ADE20K,　CamVid,　and　BSD500　datasets　demonstrate　that　the　proposed　approach　can　obtain　state-of-the-art　performance　on　both　semantic　segmentation　and　boundary　detection　tasks.　(C)　2018　SPIE　and　IS&T