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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47N4R68
Repositorysid.inpe.br/sibgrapi/2022/09.30.02.43
Last Update2022:09.30.02.43.41 (UTC) jpklock@ufmg.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.30.02.43.41
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
Citation KeyFerreiraPintCast:2022:SeScGe
TitleWeaklier Supervised: Semi-automatic Scribble Generation Applied to Semantic Segmentation
FormatOn-line
Year2022
Access Date2024, July 27
Number of Files1
Size6896 KiB
2. Context
Author1 Ferreira, Joćo Pedro Klock
2 Pinto, Joćo Paulo Lara
3 Castro, Cristiano Leite
Affiliation1 Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais
2 Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais
3 Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais
e-Mail Addressjpklock@ufmg.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2022-09-30 02:43:41 :: jpklock@ufmg.br -> administrator ::
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsWeak-supervision
semi-supervision
scribbles
semantic segmentation
remote sensing
AbstractWith many applications regarding semantic segmentation arising, along with the advent of the Deep Semantic Segmentation Networks, the need for large labeled datasets has also largely increased. But labeling thousands of images can be very expensive and time-consuming. Approaches such as weak and semi supervision try do deal with this problem, but the first cannot deal with large datasets and the latter is hard to deal with semantic segmentation. Therefore, in this work we propose a combination of both to create a novel pipeline of weak supervision, with focus in satellite imagery, capable of dealing with large datasets. We propose a pipeline to automatically generate scribbles in images, requiring that the user only label 10% of the images in a given dataset, while a classifier deal with the remaining images. Along with that, we also propose a simple semantic segmentation pipeline, that uses only images with scribbles to train a network. Results show that performance is lower, but similar to a fully supervised pipeline.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Weaklier Supervised: Semi-automatic...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47N4R68
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47N4R68
Languageen
Target FileFerreira-5-no-copyright.pdf
User Groupjpklock@ufmg.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 19
sid.inpe.br/banon/2001/03.30.15.38.24 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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