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
this https URL

本文提出了基于 Kronecker 卷积和树形特征聚合 (TFA) 模块的树形 Kronecker 卷积网络 (TKCN)，旨在解决现有的语义分割方法在扩大接受区域时忽略局部信息的问题，能够同时捕获局部信息和扩大滤波器的接收域，进一步实现多尺度物体的表示学习和复杂场景中的分层上下文信息编码。