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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CQUPS
Repositorysid.inpe.br/sibgrapi/2021/09.06.00.58
Last Update2021:09.06.00.58.09 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.00.58.09
Metadata Last Update2022:06.14.00.00.27 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00063
Citation KeyAlmeidaPerValAlmPed:2021:ReLeIm
TitleRepresentation Learning for Image Retrieval through 3D CNN and Manifold Ranking
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size4754 KiB
2. Context
Author1 Almeida, Lucas Barbosa de
2 Pereira-Ferrero, Vanessa Helena
3 Valem, Lucas Pascotti
4 Almeida, Jurandy
5 Pedronette, Daniel Carlos Guimarães
Affiliation1 UNESP 
2 UNESP 
3 UNESP 
4 UNIFESP 
5 UNESP
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 Addressbarbosa.almeida@unesp.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)2021-09-06 00:58:09 :: barbosa.almeida@unesp.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:19:18 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:27 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsimage retrieval
representation learning
manifold learning
AbstractDespite of the substantial success of Convolutional Neural Networks (CNNs) on many recognition and representation tasks, such models are very reliant on huge amount of data to allow effective training. In order to improve the generalization ability of CNNs, several approaches have been proposed, including variations of data augmentation strategies. With the goal of achieving more effective retrieval results on unsupervised learning scenarios, we propose a representation learning approach which exploits a rank-based formulation to build a more comprehensive data representation. The proposed model uses 2D and 3D CNNs trained by transfer learning and fuse both representations through a rank-based formulation based on manifold learning algorithms. Our approach was evaluated on an unsupervised image retrieval scenario applied to action recognition datasets. The experimental results indicated that significant effectiveness gains can be obtained on various datasets, reaching +56.93% of relative gains on MAP scores.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Representation Learning for...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Representation Learning for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CQUPS
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CQUPS
Languageen
Target FileSIBGRAPI_2021_Camera_Ready.pdf
User Groupbarbosa.almeida@unesp.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 4
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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