Semantic Flow for Fast and Accurate Scene Parsing
Xiangtai Li, Ansheng You, Zhen Zhu, Houlong Zhao, Maoke Yang, Kuiyuan Yang, Shaohua Tan, Yunhai Tong
Abstract
In this paper, we focus on designing effective method for fast and accurate scene parsing. A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation. Two strategies are widely used---atrous convolutions and feature pyramid fusion, are either computation intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels, and broadcast high-level features to high resolution features effectively and efficiently. Furthermore, integrating our module to a common feature pyramid structure exhibits superior performance over other real-time methods even on light-weight backbone networks, such as ResNet-18. Extensive experiments are conducted on several challenging datasets, including Cityscapes, PASCAL Context, ADE20K and CamVid. Especially, our network is the first to achieve 80.4\% mIoU on Cityscapes with a frame rate of 26 FPS. The code is available at \url{https://github.com/donnyyou/torchcv}."
BibTeX
@inproceedings{eccv2020_semanticflowforf,
title = {Semantic Flow for Fast and Accurate Scene Parsing},
author = {Xiangtai Li and Ansheng You and Zhen Zhu and Houlong Zhao and Maoke Yang and Kuiyuan Yang and Shaohua Tan and Yunhai Tong},
booktitle = {ECCV 2020},
year = {2020}
}