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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CKLG2
Repositorysid.inpe.br/sibgrapi/2021/09.04.19.57
Last Update2021:09.04.19.57.36 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.04.19.57.36
Metadata Last Update2022:06.14.00.00.25 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00056
Citation KeyDouradoNetoGuthCampWeig:2021:DoAdHo
TitleDomain Adaptation for Holistic Skin Detection
FormatOn-line
Year2021
Access Date2024, Apr. 18
Number of Files1
Size1718 KiB
2. Context
Author1 Dourado Neto, Aloisio
2 Guth, Frederico
3 Campos, Teofilo de
4 Weigang, Li
Affiliation1 Universidade de Brasília 
2 Universidade de Brasília 
3 Universidade de Brasília 
4 Universidade de Brasília
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressaloisio.dourado.bh@gmail.com.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-04 19:57:36 :: aloisio.dourado.bh@gmail.com.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:24:27 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:25 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordscomputer vision
deep learning
semantic segmentation
skin detection
domain adaptation
AbstractHuman skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. However, we found that the lack of contextual information may hinder the performance of local approaches. In this paper, we present a comprehensive evaluation of holistic and local Convolutional Neural Network (CNN) approaches on in-domain and cross-domain experiments and compare them with state-of-the-art pixel-based approaches. We also propose combining inductive transfer learning and unsupervised domain adaptation methods evaluated on different domains under several amounts of labelled data availability. We show a clear superiority of CNN over pixel-based approaches even without labeled training samples on the target domain and provide experimental support for the superiority of holistic over local approaches for human skin detection.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Domain Adaptation for...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Domain Adaptation for...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CKLG2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CKLG2
Languageen
Target FileSIBGRAP_paper_39__Domain_Adaptation_for_Holistic_Skin_Detection.pdf
User Groupaloisio.dourado.bh@gmail.com.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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