Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the input sizes are large. In the segmentation network, apart from the pyramid backbone network, an extra decoder network is often employed to recover the spatial detail information. In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks. Specifically speaking, we use the residual features to learn the edges and details, and we also use the top-level feature to learn the coarse segmentation result. At the testing phase, the predicted residuals are used to enhance the details of the coarse segmentation result. Residual learning blocks split the network into several shallow sub-networks by level-wise training, which facilitates the gradient propagation in the RPNet. We then evaluate the proposed method and compare it with the recent state-of-the-art methods on CamVid and Cityscapes datasets. The proposed single-shot segmentation based on the RPNet achieves impressive results with high efficiency on the pixel-level segmentation task.
In semantic segmentation tasks, there are also many attempts of residual learning. LRR @cite_17 uses low-resolution results to generate a boundary mask for high-resolution feature. Then high-resolution boundary is predicted to refine the result. Global-Local-Refinement @cite_2 iteratively predicts global residuals using the input which concatenates the original image and confidential map.
@cite_2 @cite_17
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Many machine vision applications require predictions for every pixel of the input image (for example semantic segmentation, boundary detection). Models for such problems usually consist of encoders which decreases spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. Therefore this paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise prediction tasks. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce a novel decoder: bilinear additive upsampling. (3) We introduce new residual-like connections for decoders. (4) We identify two decoder types which give a consistently high performance. 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.