Learning from Weak and Noisy Labels for Semantic Segmentation
A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites suc...
Uloženo v:
| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník 39; číslo 3; s. 486 - 500 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.03.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0162-8828, 2160-9292, 1939-3539 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these `free' tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L 1 -optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L 1 -optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy. |
|---|---|
| AbstractList | A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these `free' tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L1-optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L1-optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy. A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these `free' tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L 1 -optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L 1 -optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy. A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these `free' tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L -optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L -optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy. |
| Author | Peng Han Zhiwu Lu Liwei Wang Xin Gao Tao Xiang Zhenyong Fu |
| Author_xml | – sequence: 1 givenname: Zhiwu surname: Lu fullname: Lu, Zhiwu – sequence: 2 givenname: Zhenyong surname: Fu fullname: Fu, Zhenyong – sequence: 3 givenname: Tao orcidid: 0000-0002-2530-1059 surname: Xiang fullname: Xiang, Tao – sequence: 4 givenname: Peng surname: Han fullname: Han, Peng – sequence: 5 givenname: Liwei surname: Wang fullname: Wang, Liwei – sequence: 6 givenname: Xin surname: Gao fullname: Gao, Xin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28113885$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU1P3DAQhi1EBQvlD4BUReLSSxaPHX_k0MMK0YK0LZW6VY-R44yRIbHBzh749812Fw576Mkj-XlnRs-ckMMQAxJyDnQOQOur1c_F97s5oyDnTAgGih2QGQNJy5rV7JDMph9Was30MTnJ-ZFSqATlR-SYaQCutZiRL0s0KfjwULgUh-IPmqfChK74EX1-LZamxT4XLqbiFw4mjN5OxcOAYTSjj-Ej-eBMn_Fs956S319vVte35fL-2931YlnaCtRYOimZrLoOOXBUympuAa1QrXHC1ZJSzirbdugUyE4jKitqgU5QACucbvkp-bzt-5ziyxrz2Aw-W-x7EzCucwNaggTOmZrQyz30Ma5TmLbbUJKruq7kRH3aUet2wK55Tn4w6bV5EzMBbAvYFHNO6N4RoM3GfvPPfrOx3-zsTyG9F7J-K2pMxvf_j15sox4R32ep6V6gFP8LqPGQHg |
| CODEN | ITPIDJ |
| CitedBy_id | crossref_primary_10_1016_j_media_2021_102214 crossref_primary_10_1109_ACCESS_2019_2905879 crossref_primary_10_1109_TGRS_2018_2823866 crossref_primary_10_1109_TGRS_2018_2848725 crossref_primary_10_1109_TIP_2018_2881928 crossref_primary_10_1109_TMM_2019_2955240 crossref_primary_10_1016_j_neunet_2021_06_012 crossref_primary_10_1016_j_cageo_2021_104843 crossref_primary_10_1016_j_asoc_2024_112138 crossref_primary_10_1145_3480970 crossref_primary_10_1145_3572916 crossref_primary_10_1007_s10994_021_06008_4 crossref_primary_10_1007_s10278_025_01408_7 crossref_primary_10_1109_TPAMI_2020_3047817 crossref_primary_10_1007_s10462_019_09792_7 crossref_primary_10_1109_TGRS_2021_3068280 crossref_primary_10_1109_TNNLS_2018_2792062 crossref_primary_10_1016_j_cmpb_2019_05_010 crossref_primary_10_1109_TIP_2021_3089943 crossref_primary_10_1007_s10707_021_00459_6 crossref_primary_10_3390_rs14071561 crossref_primary_10_1109_ACCESS_2020_2989200 crossref_primary_10_3390_rs12203421 crossref_primary_10_1016_j_bspc_2023_105473 crossref_primary_10_1109_TPAMI_2018_2852750 crossref_primary_10_1016_j_neuroimage_2021_118568 crossref_primary_10_1080_01431161_2021_1973685 crossref_primary_10_1109_TNNLS_2021_3067107 crossref_primary_10_1109_TIP_2019_2936649 crossref_primary_10_1007_s11554_018_0762_3 crossref_primary_10_3390_app122111226 crossref_primary_10_1007_s11042_023_15983_w crossref_primary_10_1016_j_knosys_2021_106771 crossref_primary_10_3389_fnins_2021_610122 crossref_primary_10_1016_j_neucom_2018_10_061 crossref_primary_10_1016_j_bdr_2021_100272 crossref_primary_10_1109_JSTARS_2020_2994162 crossref_primary_10_3390_rs11232823 crossref_primary_10_1016_j_eswa_2022_117030 crossref_primary_10_1016_j_swevo_2017_11_003 crossref_primary_10_1109_TIP_2018_2836306 crossref_primary_10_1016_j_isprsjprs_2023_01_021 crossref_primary_10_1007_s11042_020_09730_8 crossref_primary_10_1007_s11263_021_01553_w crossref_primary_10_1007_s00521_021_06378_9 crossref_primary_10_1109_LGRS_2018_2842792 crossref_primary_10_1109_TIP_2018_2877939 crossref_primary_10_1109_TPAMI_2019_2941684 crossref_primary_10_1109_TGRS_2019_2961141 crossref_primary_10_1190_geo2018_0028_1 crossref_primary_10_1016_j_engappai_2020_103708 crossref_primary_10_1016_j_asoc_2018_10_035 crossref_primary_10_3390_rs16122080 crossref_primary_10_1016_j_patcog_2021_108467 crossref_primary_10_3390_math9192498 crossref_primary_10_1109_TGRS_2023_3264232 crossref_primary_10_3390_app13137966 crossref_primary_10_1109_TGRS_2018_2867444 crossref_primary_10_1155_2019_9180391 crossref_primary_10_1109_TMM_2020_2991592 |
| Cites_doi | 10.1109/TIP.2007.911828 10.1109/CVPR.2010.5540060 10.1109/CVPR.2012.6247719 10.1109/CVPR.2014.415 10.1137/090777761 10.1007/s11263-012-0574-z 10.1109/CVPR.2007.383098 10.1109/TNNLS.2013.2292894 10.1109/CVPR.2015.7298888 10.1109/CVPR.2015.7299002 10.1007/978-3-319-10599-4_33 10.1109/CVPR.2015.7298965 10.1109/CVPR.2015.7298780 10.5244/C.29.29 10.1109/CVPR.2008.4587503 10.1109/TPAMI.2012.231 10.1145/1631272.1631305 10.1109/ICIP.2012.6467501 10.1007/s11263-010-0344-8 10.1109/CVPR.2012.6247757 10.1109/CVPR.2013.386 10.1109/TPAMI.2008.79 10.1145/3065386 10.1007/s11263-009-0245-x 10.1109/ICCV.2011.6126299 10.1109/TMM.2013.2285526 10.1109/CVPR.2014.53 10.1109/CVPR.2014.81 10.1109/CVPR.2014.408 10.1007/978-3-319-10584-0_28 10.1109/TPAMI.2011.131 10.1109/CVPR.2014.190 10.1007/s11263-011-0449-8 10.1109/CVPR.2013.270 10.1109/CVPR.2014.310 10.1109/TMM.2011.2174780 10.1109/CVPR.2014.119 10.1109/TIP.2006.881969 10.1145/1631272.1631291 10.1109/ICCV.2011.6126219 10.1007/s11263-008-0202-0 10.1109/TPAMI.2015.2456887 10.1109/CVPR.2014.49 10.1109/ICCV.2009.5459248 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TPAMI.2016.2552172 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Technology Research Database PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 2160-9292 1939-3539 |
| EndPage | 500 |
| ExternalDocumentID | 28113885 10_1109_TPAMI_2016_2552172 7450177 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: 973 Program of China grantid: 2014CB340403; 2015CB352502 – fundername: KAUST funderid: 10.13039/501100004052 – fundername: Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China grantid: 15XNLQ01 – fundername: European Research Council FP7 Project SUNNY grantid: 313243 funderid: 10.13039/501100000781 – fundername: National Natural Science Foundation of China grantid: 61573363; 61573026 funderid: 10.13039/501100001809 – fundername: IBM Global SUR Award Program funderid: 10.13039/100004316 |
| GroupedDBID | --- -DZ -~X .DC 0R~ 29I 4.4 53G 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 E.L EBS EJD F5P HZ~ IEDLZ IFIPE IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 UHB ~02 AAYXX CITATION 5VS 9M8 ABFSI ADRHT AETEA AETIX AGSQL AI. AIBXA ALLEH FA8 H~9 IBMZZ ICLAB IFJZH NPM RIG RNI RZB VH1 XJT 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c417t-f66264dde313e77c83c1ec57baf5f9600324cbdef716d8ee7c595ef5011c5f8b3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 89 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000395555100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0162-8828 |
| IngestDate | Sun Sep 28 16:18:21 EDT 2025 Sun Nov 30 04:07:21 EST 2025 Mon Jul 21 05:42:28 EDT 2025 Sat Nov 29 05:15:57 EST 2025 Tue Nov 18 22:18:34 EST 2025 Wed Aug 27 02:47:49 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c417t-f66264dde313e77c83c1ec57baf5f9600324cbdef716d8ee7c595ef5011c5f8b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-2530-1059 |
| PMID | 28113885 |
| PQID | 1866379946 |
| PQPubID | 85458 |
| PageCount | 15 |
| ParticipantIDs | ieee_primary_7450177 crossref_primary_10_1109_TPAMI_2016_2552172 proquest_miscellaneous_1861613327 pubmed_primary_28113885 crossref_citationtrail_10_1109_TPAMI_2016_2552172 proquest_journals_1866379946 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-03-01 |
| PublicationDateYYYYMMDD | 2017-03-01 |
| PublicationDate_xml | – month: 03 year: 2017 text: 2017-03-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on pattern analysis and machine intelligence |
| PublicationTitleAbbrev | TPAMI |
| PublicationTitleAlternate | IEEE Trans Pattern Anal Mach Intell |
| PublicationYear | 2017 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 hariharan (ref37) 0 ref58 ref14 ref53 ref52 ref11 fergus (ref50) 0 ref10 chen (ref38) 2015 ref17 ref16 ref19 lucchi (ref63) 0 ref18 rubinstein (ref66) 0 ladicky (ref5) 0 ref51 gould (ref8) 0 sermanet (ref55) 0 ref48 ref47 ref42 ref44 li (ref33) 2009 ref43 everingham (ref56) 2007 xie (ref59) 0 shotton (ref57) 0 ref7 ref9 ref4 ref3 ref6 ref40 ref35 ref34 ref36 ref31 ref30 ref32 ref2 zhu (ref1) 2015 lee (ref25) 0 zhu (ref45) 0 zhou (ref46) 0 ref68 ref24 ref67 ref23 ref20 chen (ref26) 0 ref22 ref65 ref21 liu (ref49) 0 zhang (ref15) 0 ref28 ref27 ref29 pathak (ref39) 0 deng (ref54) 0 yao (ref64) 0 ref60 ref62 ref61 yuan (ref41) 0 |
| References_xml | – ident: ref23 doi: 10.1109/TIP.2007.911828 – ident: ref10 doi: 10.1109/CVPR.2010.5540060 – ident: ref40 doi: 10.1109/CVPR.2012.6247719 – ident: ref6 doi: 10.1109/CVPR.2014.415 – ident: ref48 doi: 10.1137/090777761 – year: 2015 ident: ref38 article-title: Semantic image segmentation with deep convolutional nets and fully connected CRFs publication-title: ICLRE – ident: ref67 doi: 10.1007/s11263-012-0574-z – start-page: 105 year: 0 ident: ref26 article-title: Smoothing proximal gradient method for general structured sparse learning publication-title: Proc Conf Annu Conf Uncertainty Artif Intell – ident: ref9 doi: 10.1109/CVPR.2007.383098 – year: 0 ident: ref55 article-title: OverFeat: Integrated recognition, localization and detection using convolutional networks publication-title: ICLRE – ident: ref44 doi: 10.1109/TNNLS.2013.2292894 – ident: ref19 doi: 10.1109/CVPR.2015.7298888 – start-page: 2853 year: 0 ident: ref59 article-title: Semantic graph construction for weakly-supervised image parsing publication-title: Proc 28th Nat Conf Artif Intell – ident: ref18 doi: 10.1109/CVPR.2015.7299002 – ident: ref7 doi: 10.1007/978-3-319-10599-4_33 – start-page: 912 year: 0 ident: ref45 article-title: Semi-supervised learning using Gaussian fields and harmonic functions publication-title: Proc Int Conf Mach Learn – start-page: 663 year: 0 ident: ref49 article-title: Robust subspace segmentation by low-rank representation publication-title: Proc Int Conf Mach Learn – start-page: 297 year: 0 ident: ref37 article-title: Simultaneous detection and segmentation publication-title: Proc 13th Eur Conf Comput Vis – ident: ref31 doi: 10.1109/CVPR.2015.7298965 – ident: ref32 doi: 10.1109/CVPR.2015.7298780 – ident: ref65 doi: 10.5244/C.29.29 – ident: ref2 doi: 10.1109/CVPR.2008.4587503 – start-page: 1 year: 0 ident: ref57 article-title: Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation publication-title: Proc 9th Eur Conf Comput Vis – ident: ref36 doi: 10.1109/TPAMI.2012.231 – year: 0 ident: ref39 article-title: Fully convolutional multi-class multiple instance learning publication-title: ICLRE – ident: ref20 doi: 10.1145/1631272.1631305 – start-page: 695 year: 0 ident: ref41 article-title: A novel topic-level random walk framework for scene image co-segmentation publication-title: Proc 13th Eur Conf Comput Vis – start-page: 702 year: 0 ident: ref64 article-title: Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation publication-title: Proc IEEE Conf Comput Vis Pattern Recog – ident: ref43 doi: 10.1109/ICIP.2012.6467501 – ident: ref27 doi: 10.1007/s11263-010-0344-8 – start-page: 400 year: 0 ident: ref63 article-title: Structured image segmentation using kernelized features publication-title: Proc 12th Eur Conf Comput Vis – ident: ref12 doi: 10.1109/CVPR.2012.6247757 – ident: ref68 doi: 10.1109/CVPR.2013.386 – ident: ref24 doi: 10.1109/TPAMI.2008.79 – ident: ref35 doi: 10.1145/3065386 – start-page: 1889 year: 0 ident: ref15 article-title: Sparse reconstruction for weakly supervised semantic segmentation publication-title: Proc 23rd Int Joint Conf Artif Intell – ident: ref61 doi: 10.1007/s11263-009-0245-x – start-page: 2036 year: 2009 ident: ref33 article-title: Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. publication-title: Proc IEEE Conf Comput Vis Pattern Recog – ident: ref11 doi: 10.1109/ICCV.2011.6126299 – ident: ref16 doi: 10.1109/TMM.2013.2285526 – start-page: 632 year: 0 ident: ref8 article-title: Superpixel graph label transfer with learned distance metric publication-title: Proc 13th Eur Conf Comput Vis – ident: ref30 doi: 10.1109/CVPR.2014.53 – ident: ref53 doi: 10.1109/CVPR.2014.81 – ident: ref17 doi: 10.1109/CVPR.2014.408 – ident: ref21 doi: 10.1007/978-3-319-10584-0_28 – year: 2007 ident: ref56 article-title: The PASCAL visual object classes challenge 2007 (VOC2007) Results – ident: ref58 doi: 10.1109/TPAMI.2011.131 – start-page: 321 year: 0 ident: ref46 article-title: Learning with local and global consistency publication-title: Proc Adv Neural Inf Process Syst – ident: ref42 doi: 10.1109/CVPR.2014.190 – ident: ref62 doi: 10.1007/s11263-011-0449-8 – ident: ref29 doi: 10.1007/s11263-012-0574-z – ident: ref14 doi: 10.1109/CVPR.2013.270 – ident: ref52 doi: 10.1109/CVPR.2014.310 – ident: ref13 doi: 10.1109/TMM.2011.2174780 – ident: ref34 doi: 10.1109/CVPR.2014.119 – ident: ref22 doi: 10.1109/TIP.2006.881969 – ident: ref60 doi: 10.1145/1631272.1631291 – start-page: 85 year: 0 ident: ref66 article-title: Annotation propagation in large image databases via dense image correspondence publication-title: Proc 12th Eur Conf Comput Vis – ident: ref28 doi: 10.1109/ICCV.2011.6126219 – start-page: 248 year: 0 ident: ref54 article-title: ImageNet: A large-scale hierarchical image database publication-title: Proc IEEE Conf Comput Vis Pattern Recog – ident: ref3 doi: 10.1007/s11263-008-0202-0 – year: 2015 ident: ref1 article-title: Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation publication-title: CoRR – ident: ref47 doi: 10.1109/TPAMI.2015.2456887 – ident: ref51 doi: 10.1109/CVPR.2014.49 – start-page: 522 year: 0 ident: ref50 article-title: Semi-supervised learning in gigantic image collections publication-title: Proc Adv Neural Inf Process Syst – start-page: 801 year: 0 ident: ref25 article-title: Efficient sparse coding algorithms publication-title: Proc Adv Neural Inf Process Syst – ident: ref4 doi: 10.1109/ICCV.2009.5459248 – start-page: 239 year: 0 ident: ref5 article-title: Graph cut based inference with co-occurrence statistics publication-title: Proc 11th Eur Conf Comput Vis |
| SSID | ssj0014503 |
| Score | 2.5544648 |
| Snippet | A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels.... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 486 |
| SubjectTerms | Algorithms Computational modeling Image annotation Image segmentation label noise reduction Labeling Labels Machine learning Noise measurement Noise reduction Optimization Pixels Semantic segmentation Semantics sparse learning Training weakly supervised learning |
| Title | Learning from Weak and Noisy Labels for Semantic Segmentation |
| URI | https://ieeexplore.ieee.org/document/7450177 https://www.ncbi.nlm.nih.gov/pubmed/28113885 https://www.proquest.com/docview/1866379946 https://www.proquest.com/docview/1861613327 |
| Volume | 39 |
| WOSCitedRecordID | wos000395555100006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2160-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014503 issn: 0162-8828 databaseCode: RIE dateStart: 19790101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1RT9wwDLYA8bA9jAEbu42hTOJtK1ybpkkf9oCmoSGxExJMu7eqcVx0AnqIu0Pav5-TphUP26S9RYqbtrFd23X8GeDQlrYmp2WCKRZJblEnNpNF4sauzpXKrFM2NJvQk4mZTsuLNfg01MIQUTh8Rkd-GHL5bo4r_6vsWOeKBUivw7rWRVerNWQMeEp2ON6s4RxG9AUy4_L46uLk-5k_xVUcsQPtOzJ5CGCTptL4FspP7FFosPJ3XzPYnNOt_3val_Ai-pbipBOGbVijdge2-r4NIqrxDjx_AkK4C58jxOq18KUm4ifVN6JunZjMZ4tf4ry2bD0Fu7biku6YDTPkwfVdLFlqX8GP069XX74lsalCgnmql0lTcAiT80dNppK0RiMxJVTa1o1qOJwZs4eF1lHDgZQzRBpVqajhV0lRNcbK17DRzlt6A8Lj9DhjMGOaXLOfldsSrfKZTonsSY0g7be2wog47htf3FYh8hiXVeBM5TlTRc6M4ONwzX2Ht_FP6l2_7wNl3PIR7PccrKJKLiqP7Cd1WebFCD4M06xMPkNStzRfBRr2gKXMeIm9jvPD2r3AvP3zPd_Bs8xb_HA8bR82lg8reg-b-LicLR4OWGKn5iBI7G_OUuL8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB1RilQ4lPJR2EKLK_XWBpLYjpNDD6gqArGskLpVuUWxPUGrQhaxu0j8-44dJ-JQKvVmyY6TeGbiNxnPG4BPutAVWsUjk5gsEtqoSKc8i2xsKyFlqq3UvtiEGo3yq6vicgm-9LkwiOgPn-Gha_pYvp2ahftVdqSEJAVSL-ClFCKN22ytPmZAnbxl8iYbJ0eiS5GJi6Px5fHFmTvHlR0ShHY1mRwJcJ4kPHdFlJ_sSL7EyvNo0-86J-v_97xv4HVAl-y4VYcNWMJmE9a7yg0sGPImrD2hIdyCr4Fk9Zq5ZBP2C6vfrGosG00ns0c2rDTtn4zALfuBtySIiaHG9W1IWmq24efJ9_G30yiUVYiMSNQ8qjNyYgR91njCUSmTc5OgkUpXtazJoYkJYxltsSZXyuaIyshCYk2vkhhZ55q_heVm2uAuMMfUY_PcpDRGKEJaQhdGSxfr5Iaw1ACSbmlLEzjHXemLm9L7HnFResmUTjJlkMwAPvfX3LWMG_8cveXWvR8ZlnwA-50Ey2CUs9Jx-3FVFCIbwMe-m8zJxUiqBqcLP4YwMOcpTbHTSr6fu1OYd3-_5wG8Oh1fDMvh2eh8D1bbnH13GG0fluf3C3wPK-ZhPpndf_B6-wdtPeVZ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Learning+from+Weak+and+Noisy+Labels+for+Semantic+Segmentation&rft.jtitle=IEEE+transactions+on+pattern+analysis+and+machine+intelligence&rft.au=Lu%2C+Zhiwu&rft.au=Fu%2C+Zhenyong&rft.au=Xiang%2C+Tao&rft.au=Han%2C+Peng&rft.date=2017-03-01&rft.issn=0162-8828&rft.eissn=2160-9292&rft.volume=39&rft.issue=3&rft.spage=486&rft.epage=500&rft_id=info:doi/10.1109%2FTPAMI.2016.2552172&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TPAMI_2016_2552172 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0162-8828&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0162-8828&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0162-8828&client=summon |