Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1704.08545 v2 pith:AX2K4ZNU submitted 2017-04-27 cs.CV

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

Hengshuang Zhao , Xiaojuan Qi , Xiaoyong Shen , Jianping Shi , Jiaya Jia This is my paper
classification cs.CV
keywords real-timesegmentationcascadechallengingicnetinferencelabelsemantic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Separable Convolutional LSTMs for Faster Video Segmentation

    cs.CV 2019-07 unverdicted novelty 6.0

    Separable convLSTMs cut parameters and FLOPs in video segmentation, delivering up to 15% faster GPU inference with similar or slightly lower accuracy.

  2. Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

    cs.CV 2019-07 unverdicted novelty 4.0

    Learned adaptive downsampling for semantic segmentation that prioritizes locations near semantic boundaries to improve the accuracy-efficiency trade-off over uniform sampling.

  3. ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation

    cs.CV 2019-06 unverdicted novelty 4.0

    ESNet is a lightweight symmetric CNN using factorized residual units and parallel dilated convolutions that reaches over 62 FPS semantic segmentation on Cityscapes with 1.6M parameters.