  Context is essential for semantic segmentation. Due to the diverse shapes of
objects and their complex layout in various scene images, the spatial scales
and shapes of contexts for different objects have very large variation. It is
thus ineffective or inefficient to aggregate various context information from a
predefined fixed region. In this work, we propose to generate a scale- and
shape-variant semantic mask for each pixel to confine its contextual region. To
this end, we first propose a novel paired convolution to infer the semantic
correlation of the pair and based on that to generate a shape mask. Using the
inferred spatial scope of the contextual region, we propose a shape-variant
convolution, of which the receptive field is controlled by the shape mask that
varies with the appearance of input. In this way, the proposed network
aggregates the context information of a pixel from its semantic-correlated
region instead of a predefined fixed region. Furthermore, this work also
proposes a labeling denoising model to reduce wrong predictions caused by the
noisy low-level features. Without bells and whistles, the proposed segmentation
network achieves new state-of-the-arts consistently on the six public
segmentation datasets.
