Method Details

Details for method 'TKCN'

name	TKCN
challenge	pixel-level semantic labeling
details	Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect important local contextual information. To tackle this issue, we firstly propose a novel Kronecker convolution which adopts Kronecker product to expand its kernel for taking into account the feature vectors neglected by atrous convolutions. Therefore, it can capture local contextual information and enlarge the field of view 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) that employs Kronecker convolution and TFA module. Extensive experiments on three datasets, PASCAL VOC 2012, PASCAL-Context and Cityscapes, verify the e Submission was previously published with name "TKCNNet".
publication	Tree-structured Kronecker Convolutional Networks for Semantic Segmentation Tianyi Wu, Sheng Tang, Rui Zhang Linghui Li, Yongdong Zhang
project page / code
used Cityscapes data	fine annotations
used external data	ImageNet
runtime	1 s K80
subsampling	no
submission date	April, 2018
previous submissions	1