TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Real-Time Semantic Segmentation	Cityscapes test	SFNet-R18	mIoU	80.4%	# 2
Real-Time Semantic Segmentation	Cityscapes test	SFNet-R18	Time (ms)	39.2	# 20
Real-Time Semantic Segmentation	Cityscapes test	SFNet-R18	Frame (fps)	25.7(1080Ti)	# 27

Badge	Markdown
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semantic-flow-for-fast-and-accurate-scene/real-time-semantic-segmentation-on-cityscapes)](https://paperswithcode.com/sota/real-time-semantic-segmentation-on-cityscapes?p=semantic-flow-for-fast-and-accurate-scene)`

Semantic Flow for Fast and Accurate Scene Parsing

ECCV 2020 · Xiangtai Li, Ansheng You, Zhen Zhu, Houlong Zhao, Maoke Yang, Kuiyuan Yang, Yunhai Tong ·

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/lxtGH/SFSegNets}.