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.
Zhang REF proposed to use context to refine the inconsistent parsing results iteratively.
Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
IJCAI
Computer Science