We focus on the challenging task of real-time 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 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 unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.
@article{arxiv.1704.08545,
title = {ICNet for Real-Time Semantic Segmentation on High-Resolution Images},
author = {Hengshuang Zhao and Xiaojuan Qi and Xiaoyong Shen and Jianping Shi and Jiaya Jia},
journal= {arXiv preprint arXiv:1704.08545},
year = {2018}
}