ICNet for Real-Time Semantic Segmentation on High-Resolution Images
The Chinese University of Hong Kong
arXiv:1704.08545 [cs.CV], (27 Apr 2017)
@article{zhao2017icnet,
title={ICNet for Real-Time Semantic Segmentation on High-Resolution Images},
author={Zhao, Hengshuang and Qi, Xiaojuan and Shen, Xiaoyong and Shi, Jianping and Jia, Jiaya},
year={2017},
month={apr},
archivePrefix={"arXiv"},
primaryClass={cs.CV}
}
We focus on the challenging task of realtime semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an compressed-PSPNet-based image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion to quickly achieve high-quality segmentation. Our system yields realtime inference on a single GPU card with decent quality results evaluated on challenging Cityscapes dataset.
April 30, 2017 by hgpu
