A Residual Correction Approach for Semi-supervised Semantic Segmentation

Haoliang Li; Huicheng Zheng

doi:10.1007/978-3-030-88013-2_8

A Residual Correction Approach for Semi-supervised Semantic Segmentation

Haoliang Li, Huicheng Zheng^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

Fully-supervised deep learning models have achieved a great success in complex semantic segmentation tasks. However, the segmentation annotations are prohibitively expensive, which causes a growing interest in the methods that require lower annotating cost but still achieve a competitive performance. This paper proposes a residual correction approach based on self-training for semi-supervised semantic segmentation. We train a residual correction network built on top of the segmentation network with labeled data to predict a residual of the original segmentation. For unlabeled data, the output of the residual correction network is combined with the original segmentation to form the pseudo label used to train the segmentation network. Extensive experimental results on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	Pattern Recognition and Computer Vision
Subtitle of host publication	4th Chinese Conference, PRCV 2021, Proceedings, Part IV
Editors	Huimin Ma, Liang Wang, Changshui Zhang, Fei Wu, Tieniu Tan, Yaonan Wang, Jianhuang Lai, Yao Zhao
Publisher	Springer, Cham
Pages	90-102
Edition	1
ISBN (Electronic)	978-3-030-88013-2
ISBN (Print)	978-3-030-88012-5
DOIs	https://doi.org/10.1007/978-3-030-88013-2_8
Publication status	Published - 2021
Externally published	Yes
Event	4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021 - Beijing, China Duration: 29 Oct 2021 → 1 Nov 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13022
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021
Place	China
City	Beijing
Period	29/10/21 → 1/11/21

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61976231, Grant U1611461, Grant 61573387, and Grant 61172141, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011869, and in part by the Science and Technology Program of Guangzhou under Grant 201803030029.

Research Keywords

Self-training
Semantic segmentation
Semi-supervised learning

Access Output

10.1007/978-3-030-88013-2_8

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Li, H., & Zheng, H. (2021). A Residual Correction Approach for Semi-supervised Semantic Segmentation. In H. Ma, L. Wang, C. Zhang, F. Wu, T. Tan, Y. Wang, J. Lai, & Y. Zhao (Eds.), Pattern Recognition and Computer Vision : 4th Chinese Conference, PRCV 2021, Proceedings, Part IV (1 ed., pp. 90-102). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13022). Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_8

Li, Haoliang ; Zheng, Huicheng. / A Residual Correction Approach for Semi-supervised Semantic Segmentation. Pattern Recognition and Computer Vision : 4th Chinese Conference, PRCV 2021, Proceedings, Part IV. editor / Huimin Ma ; Liang Wang ; Changshui Zhang ; Fei Wu ; Tieniu Tan ; Yaonan Wang ; Jianhuang Lai ; Yao Zhao. 1. ed. Springer, Cham, 2021. pp. 90-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Residual Correction Approach for Semi-supervised Semantic Segmentation",

abstract = "Fully-supervised deep learning models have achieved a great success in complex semantic segmentation tasks. However, the segmentation annotations are prohibitively expensive, which causes a growing interest in the methods that require lower annotating cost but still achieve a competitive performance. This paper proposes a residual correction approach based on self-training for semi-supervised semantic segmentation. We train a residual correction network built on top of the segmentation network with labeled data to predict a residual of the original segmentation. For unlabeled data, the output of the residual correction network is combined with the original segmentation to form the pseudo label used to train the segmentation network. Extensive experimental results on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the effectiveness of the proposed approach.",

keywords = "Self-training, Semantic segmentation, Semi-supervised learning",

author = "Haoliang Li and Huicheng Zheng",

year = "2021",

doi = "10.1007/978-3-030-88013-2_8",

language = "English",

isbn = "978-3-030-88012-5",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Cham",

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editor = "Huimin Ma and Liang Wang and Changshui Zhang and Fei Wu and Tieniu Tan and Yaonan Wang and Jianhuang Lai and Yao Zhao",

booktitle = "Pattern Recognition and Computer Vision",

edition = "1",

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Li, H & Zheng, H 2021, A Residual Correction Approach for Semi-supervised Semantic Segmentation. in H Ma, L Wang, C Zhang, F Wu, T Tan, Y Wang, J Lai & Y Zhao (eds), Pattern Recognition and Computer Vision : 4th Chinese Conference, PRCV 2021, Proceedings, Part IV. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13022, Springer, Cham, pp. 90-102, 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, Beijing, China, 29/10/21. https://doi.org/10.1007/978-3-030-88013-2_8

A Residual Correction Approach for Semi-supervised Semantic Segmentation. / Li, Haoliang; Zheng, Huicheng.
Pattern Recognition and Computer Vision : 4th Chinese Conference, PRCV 2021, Proceedings, Part IV. ed. / Huimin Ma; Liang Wang; Changshui Zhang; Fei Wu; Tieniu Tan; Yaonan Wang; Jianhuang Lai; Yao Zhao. 1. ed. Springer, Cham, 2021. p. 90-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13022).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Residual Correction Approach for Semi-supervised Semantic Segmentation

AU - Li, Haoliang

AU - Zheng, Huicheng

PY - 2021

Y1 - 2021

N2 - Fully-supervised deep learning models have achieved a great success in complex semantic segmentation tasks. However, the segmentation annotations are prohibitively expensive, which causes a growing interest in the methods that require lower annotating cost but still achieve a competitive performance. This paper proposes a residual correction approach based on self-training for semi-supervised semantic segmentation. We train a residual correction network built on top of the segmentation network with labeled data to predict a residual of the original segmentation. For unlabeled data, the output of the residual correction network is combined with the original segmentation to form the pseudo label used to train the segmentation network. Extensive experimental results on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the effectiveness of the proposed approach.

AB - Fully-supervised deep learning models have achieved a great success in complex semantic segmentation tasks. However, the segmentation annotations are prohibitively expensive, which causes a growing interest in the methods that require lower annotating cost but still achieve a competitive performance. This paper proposes a residual correction approach based on self-training for semi-supervised semantic segmentation. We train a residual correction network built on top of the segmentation network with labeled data to predict a residual of the original segmentation. For unlabeled data, the output of the residual correction network is combined with the original segmentation to form the pseudo label used to train the segmentation network. Extensive experimental results on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the effectiveness of the proposed approach.

KW - Self-training

KW - Semantic segmentation

KW - Semi-supervised learning

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85118926055&origin=recordpage

U2 - 10.1007/978-3-030-88013-2_8

DO - 10.1007/978-3-030-88013-2_8

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-030-88012-5

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 90

EP - 102

BT - Pattern Recognition and Computer Vision

A2 - Ma, Huimin

A2 - Wang, Liang

A2 - Zhang, Changshui

A2 - Wu, Fei

A2 - Tan, Tieniu

A2 - Wang, Yaonan

A2 - Lai, Jianhuang

A2 - Zhao, Yao

PB - Springer, Cham

T2 - 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021

Y2 - 29 October 2021 through 1 November 2021

ER -

Li H, Zheng H. A Residual Correction Approach for Semi-supervised Semantic Segmentation. In Ma H, Wang L, Zhang C, Wu F, Tan T, Wang Y, Lai J, Zhao Y, editors, Pattern Recognition and Computer Vision : 4th Chinese Conference, PRCV 2021, Proceedings, Part IV. 1 ed. Springer, Cham. 2021. p. 90-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2021 Oct 22. doi: 10.1007/978-3-030-88013-2_8

A Residual Correction Approach for Semi-supervised Semantic Segmentation

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Research Keywords

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