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

2017-04-27ECCV 2018Code Available0· sign in to hype

Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia

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Abstract

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.

Tasks

Dichotomous Image SegmentationGPUReal-Time Semantic SegmentationSegmentationSemantic SegmentationThermal Image SegmentationVocal Bursts Intensity Prediction

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Cityscapes testICNetMean IoU (class)70.6—Unverified
Trans10KICNetmIoU23.39—Unverified

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