Semantic Flow for Fast and Accurate Scene Parsing
Xiangtai Li, Ansheng You, Zhen Zhu, Houlong Zhao, Maoke Yang, Kuiyuan Yang, Yunhai Tong
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- github.com/donnyyou/torchcvOfficialIn paperpytorch★ 2,255
- github.com/lxtGH/SFSegNetsOfficialIn paperpytorch★ 385
- github.com/PaddlePaddle/PaddleSegpaddle★ 9,319
- github.com/sithu31296/semantic-segmentationpytorch★ 939
- github.com/Shualite/SFNet.pytorchpytorch★ 12
- github.com/MaybeShewill-CV/sfnet-tensorflowtf★ 0
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 https://github.com/lxtGH/SFSegNets.