ICNet for Real-Time Semantic Segmentation on High-Resolution Images

  • Hengshuang ZhaoEmail author
  • Xiaojuan Qi
  • Xiaoyong Shen
  • Jianping Shi
  • Jiaya Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)


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.


Real-time High-resolution Semantic segmentation 

Supplementary material

474178_1_En_25_MOESM1_ESM.pdf (2.6 mb)
Supplementary material 1 (pdf 2637 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hengshuang Zhao
    • 1
    Email author
  • Xiaojuan Qi
    • 1
  • Xiaoyong Shen
    • 2
  • Jianping Shi
    • 3
  • Jiaya Jia
    • 1
    • 2
  1. 1.The Chinese University of Hong KongShatinHong Kong
  2. 2.Tencent Youtu LabShenzhenChina
  3. 3.SenseTime ResearchBeijingChina

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