  Semantic segmentation, which refers to pixel-wise classification of an image,
is a fundamental topic in computer vision owing to its growing importance in
robot vision and autonomous driving industries. It provides rich information
about objects in the scene such as object boundary, category, and location.
Recent methods for semantic segmentation often employ an encoder-decoder
structure using deep convolutional neural networks. The encoder part extracts
feature of the image using several filters and pooling operations, whereas the
decoder part gradually recovers the low-resolution feature maps of the encoder
into a full input resolution feature map for pixel-wise prediction. However,
the encoder-decoder variants for semantic segmentation suffer from severe
spatial information loss, caused by pooling operations or convolutions with
stride, and does not consider the context in the scene. In this paper, we
propose a dense upsampling convolution method based on guided filtering to
effectively preserve the spatial information of the image in the network. We
further propose a novel local context convolution method that not only covers
larger-scale objects in the scene but covers them densely for precise object
boundary delineation. Theoretical analyses and experimental results on several
benchmark datasets verify the effectiveness of our method. Qualitatively, our
approach delineates object boundaries at a level of accuracy that is beyond the
current excellent methods. Quantitatively, we report a new record of 82.86% and
81.62% of pixel accuracy on ADE20K and Pascal-Context benchmark datasets,
respectively. In comparison with the state-of-the-art methods, the proposed
method offers promising improvements.
