CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision
Fan, Junsong1,2; Zhang, Zhaoxiang1,2,3
Source PublicationINTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN0920-5691
2023-08-12
Pages20
Corresponding AuthorZhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
AbstractWeakly supervised semantic segmentation (WSSS) aims to reduce the cost of collecting dense pixel-level annotations for segmentation models by adopting weak labels to train. Although WSSS methods have achieved great success, recent approaches mainly concern the image-level label-based WSSS, which is limited to object-centric datasets instead of more challenging practical datasets that contain many co-occurrent classes. In comparison, point-level labels could provide some spatial information to address the class co-occurrent confusion problem. Meanwhile, it only requires an additional click when recognizing the targets, which is of negligible annotation overhead. Thus, we choose to study utilizing point labels for the general-purpose WSSS. The main difficulty of utilizing point-level labels is bridging the gap between the sparse point-level labels and the dense pixel-level predictions. To alleviate this problem, we propose a superpixel augmented pseudo-mask generation strategy and a class-aware contrastive learning approach, which manages to recover reliable dense constraints and apply them both to the segmentation models' final prediction and the intermediate features. Diagnostic experiments on the challenging Pascal VOC, Cityscapes, and the ADE20k datasets demonstrate that our approach can efficiently and effectively compensate for the sparse point-level labels and achieve cutting-edge performance on the point-based segmentation problems.
KeywordWeakly supervised learning Semantic segmentation Deep learning
DOI10.1007/s11263-023-01862-2
WOS KeywordSALIENT OBJECT DETECTION
Indexed BySCI
Language英语
Funding ProjectMajor Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[U21B2042] ; National Natural Science Foundation of China[62072457] ; National Natural Science Foundation of China[62006231] ; InnoHK program
Funding OrganizationMajor Project for New Generation of AI ; National Natural Science Foundation of China ; InnoHK program
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:001046969200001
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/53988
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorZhang, Zhaoxiang
Affiliation1.Chinese Acad Sci HKISI CAS, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong 999077, Peoples R China
2.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp CRIPAC, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci UCAS, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fan, Junsong,Zhang, Zhaoxiang. Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:20.
APA Fan, Junsong,&Zhang, Zhaoxiang.(2023).Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision.INTERNATIONAL JOURNAL OF COMPUTER VISION,20.
MLA Fan, Junsong,et al."Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):20.
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