  In this paper, we present a joint multi-task learning framework for semantic
segmentation and boundary detection. The critical component in the framework is
the iterative pyramid context module (PCM), which couples two tasks and stores
the shared latent semantics to interact between the two tasks. For semantic
boundary detection, we propose the novel spatial gradient fusion to suppress
nonsemantic edges. As semantic boundary detection is the dual task of semantic
segmentation, we introduce a loss function with boundary consistency constraint
to improve the boundary pixel accuracy for semantic segmentation. Our extensive
experiments demonstrate superior performance over state-of-the-art works, not
only in semantic segmentation but also in semantic boundary detection. In
particular, a mean IoU score of 81:8% on Cityscapes test set is achieved
without using coarse data or any external data for semantic segmentation. For
semantic boundary detection, we improve over previous state-of-the-art works by
9.9% in terms of AP and 6:8% in terms of MF(ODS).
