CASIA OpenIR  > 智能系统与工程
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
Gao, Naiyu1,2; Shan, Yanhu3; Wang, Yupei4,5,6,7; Zhao, Xin1,2; Huang, Kaiqi1,2,8,9
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2021-02-01
Volume31Issue:2Pages:661-673
Corresponding AuthorHuang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
AbstractProposal-free instance segmentation methods mainly generate instance-agnostic semantic segmentation labels and instance-aware features to group pixels into different object instances. However, previous methods mostly employ separate modules for these two sub-tasks and require multiple passes for inference. In addition to the lack of efficiency, previous methods also failed to perform as well as proposal-based approaches. To this end, this work proposes a single-shot proposal-free instance segmentation method that requires only one single pass for prediction. Our method is based on learning an affinity pyramid, which computes the probability that two pixels belong to the same instance in a hierarchical manner. Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition (CGP) module is presented to fuse the two predictions and segment instances efficiently. As an additional contribution, we conduct an experiment to demonstrate the benefits of proposal-free methods in capturing detailed structures from finely annotated training examples. Our approach is evaluated on the Cityscapes and COCO datasets and achieves state-of-the-art performance.
KeywordSemantics Image segmentation Training Proposals Task analysis Automation Predictive models Instance segmentation affinity pyramid feature pyramid single-shot graph partition
DOI10.1109/TCSVT.2020.2985420
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61876181] ; Chinese Academy of Science[QYZDB-SSW-JSC006] ; Youth Innovation Promotion Association CAS
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Chinese Academy of Science ; Youth Innovation Promotion Association CAS
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000615044400019
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/43263
Collection智能系统与工程
Corresponding AuthorHuang, Kaiqi
Affiliation1.Chinese Acad Sci, Ctr Res Intelligent Syst & Engn, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Horizon Robot, Beijing 100000, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Beijing Inst Technol, Beijing 100081, Peoples R China
6.Beijing Inst Technol, Chongqing Innovat Ctr, Chongqing 401135, Peoples R China
7.Beijing Key Lab Embedded Real Time Informat Proc, Beijing 100081, Peoples R China
8.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
9.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, 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
Gao, Naiyu,Shan, Yanhu,Wang, Yupei,et al. SSAP: Single-Shot Instance Segmentation With Affinity Pyramid[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2021,31(2):661-673.
APA Gao, Naiyu,Shan, Yanhu,Wang, Yupei,Zhao, Xin,&Huang, Kaiqi.(2021).SSAP: Single-Shot Instance Segmentation With Affinity Pyramid.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,31(2),661-673.
MLA Gao, Naiyu,et al."SSAP: Single-Shot Instance Segmentation With Affinity Pyramid".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31.2(2021):661-673.
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