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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45D3C8H
Repositorysid.inpe.br/sibgrapi/2021/09.07.06.25
Last Update2021:09.07.06.25.04 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.07.06.25.04
Metadata Last Update2022:06.14.00.00.33 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00043
Citation KeyFlores-BenitesMugrMora:2021:SpFeAt
TitleTVAnet: a spatial and feature-based attention model for self-driving car
FormatOn-line
Year2021
Access Date2024, July 14
Number of Files1
Size1024 KiB
2. Context
Author1 Flores-Benites, Victor
2 Mugruza-Vassallo, Carlos Andrés
3 Mora-Colque, Rensso Victor Hugo
Affiliation1 Universidad Católica San Pablo 
2 Universidad Nacional Tecnológica de Lima Sur 
3 Universidad Católica San Pablo
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 Addressvictor.flores@ucsp.edu.pe
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)2021-10-04 19:18:17 :: victor.flores@ucsp.edu.pe -> administrator :: 2021
2022-03-02 00:54:16 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:24:51 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:33 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsvisual attention
self-driving
spatial attention
feature-based attention
AbstractEnd-to-end methods facilitate the development of self-driving models by employing a single network that learns the human driving style from examples. However, these models face problems of distributional shift problem, causal confusion, and high variance. To address these problems we propose two techniques. First, we propose the priority sampling algorithm, which biases the training sampling towards unknown observations for the model. Priority sampling employs a trade-off strategy that incentivizes the training algorithm to explore the whole dataset. Our results show uniform training on the dataset, as well as improved performance. As a second approach, we propose a model based on the theory of visual attention, called TVAnet, by which selecting relevant visual information to build an optimal environment representation. TVAnet employs two visual information selection mechanisms: spatial and feature-based attention. Spatial attention selects regions with visual encoding similar to contextual encoding, while feature-based attention selects features disentangled with useful information for routine driving. Furthermore, we encourage the model to recognize new sources of visual information by adding a bottom-up input. Results in the CoRL-2017 dataset show that our spatial attention mechanism recognizes regions relevant to the driving task. TVAnet builds disentangled features with low mutual dependence. Furthermore, our model is interpretable, facilitating the understanding of intelligent vehicle behavior. Finally, we report performance improvements over traditional end-to-end models.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > TVAnet: a spatial...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > TVAnet: a spatial...
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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/45D3C8H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45D3C8H
Languageen
Target File109.pdf
User Groupvictor.flores@ucsp.edu.pe
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 28
sid.inpe.br/sibgrapi/2022/06.10.21.49 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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