Cited by 862Advanced Neural Network ApplicationsMedical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

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Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

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Lin, Guosheng, Shen, Chunhua, van den Hengel, Anton, Reid, Ian·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr.2016.348

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Abstract

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.

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Authors: Lin, G., Shen, C., van den Hengel, A., & Reid, I.
Affiliations: Australian Centre for Robotic Vision (AU)
ORCID: Lin, Guosheng
Shen, Chunhua
Reid, Ian
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2016-06
Pages: 3194-3203
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2016.348
OpenAlex: W2963563573
Cited by: 862
References: 49
Citations by year: 2026: 5 · 2025: 20 · 2024: 29 · 2023: 59 · 2022: 80 · 2021: 115 · 2020: 133 · 2019: 127 · 2018: 113 · 2017: 115 · 2016: 50
FWCI: 46.86
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
arXiv (Cornell University)
Funders: Australian Centre for Robotic Vision (#CE140100016)
Australian Research Council (FL130100102, CE140100016, FT120100969)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: semantic segmentation, conditional random fields, multi-scale input, contextual information, convolutional neural networks

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Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

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