  Long-range contextual information is essential for achieving high-performance
semantic segmentation. Previous feature re-weighting methods demonstrate that
using global context for re-weighting feature channels can effectively improve
the accuracy of semantic segmentation. However, the globally-sharing feature
re-weighting vector might not be optimal for regions of different classes in
the input image. In this paper, we propose a Context-adaptive Convolution
Network (CaC-Net) to predict a spatially-varying feature weighting vector for
each spatial location of the semantic feature maps. In CaC-Net, a set of
context-adaptive convolution kernels are predicted from the global contextual
information in a parameter-efficient manner. When used for convolution with the
semantic feature maps, the predicted convolutional kernels can generate the
spatially-varying feature weighting factors capturing both global and local
contextual information. Comprehensive experimental results show that our
CaC-Net achieves superior segmentation performance on three public datasets,
PASCAL Context, PASCAL VOC 2012 and ADE20K.
