  Most existing semantic segmentation methods employ atrous convolution to
enlarge the receptive field of filters, but neglect partial information. To
tackle this issue, we firstly propose a novel Kronecker convolution which
adopts Kronecker product to expand the standard convolutional kernel for taking
into account the partial feature neglected by atrous convolutions. Therefore,
it can capture partial information and enlarge the receptive field of filters
simultaneously without introducing extra parameters. Secondly, we propose
Tree-structured Feature Aggregation (TFA) module which follows a recursive rule
to expand and forms a hierarchical structure. Thus, it can naturally learn
representations of multi-scale objects and encode hierarchical contextual
information in complex scenes. Finally, we design Tree-structured Kronecker
Convolutional Networks (TKCN) which employs Kronecker convolution and TFA
module. Extensive experiments on three datasets, PASCAL VOC 2012,
PASCAL-Context and Cityscapes, verify the effectiveness of our proposed
approach. We make the code and the trained model publicly available at
https://github.com/wutianyiRosun/TKCN.
