Most of the existing scene parsing methods suffer from the serious problems of both inconsistent parsing results and object boundary shift. To tackle these issues, we first propose a Global-residual Refinement Network (GRN) through exploiting global contextual information to predict the parsing residuals and iteratively smoothen the inconsistent parsing labels. Furthermore, we propose a Localboundary Refinement Network (LRN) to learn the position-adaptive propagation coefficients so that local contextual information from neighbors can be optimally captured for refining object boundaries. Finally, we cascade the proposed two refinement networks after a fully residual convolutional neural network within a uniform framework. Extensive experiments on ADE20K and Cityscapes datasets well demonstrate the effectiveness of the two refinement methods for refining scene parsing predictions.
However, the work REF proposes a Global-residual Refinement Network through exploiting global contextual information to predict the parsing residuals.
Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
IJCAI
Computer Science