Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision

doi:10.1007/s11263-023-01862-2

	Toward Practical Weakly Supervised Semantic Segmentation via Point-Level Supervision
	Fan, Junsong1,2 ; Zhang, Zhaoxiang1,2,3
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER VISION
ISSN	0920-5691
	2023-08-12
页码	20
通讯作者	Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn)
摘要	Weakly 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.
关键词	Weakly supervised learning Semantic segmentation Deep learning
DOI	10.1007/s11263-023-01862-2
关键词[WOS]	SALIENT OBJECT DETECTION
收录类别	SCI
语种	英语
资助项目	Major 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
项目资助者	Major Project for New Generation of AI ; National Natural Science Foundation of China ; InnoHK program
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001046969200001
出版者	SPRINGER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53988
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Zhaoxiang
作者单位	1.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
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 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.