1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/45CUSQ5 |
Repository | sid.inpe.br/sibgrapi/2021/09.06.22.26 |
Last Update | 2021:09.06.22.26.16 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2021/09.06.22.26.16 |
Metadata Last Update | 2022:06.14.00.00.32 (UTC) administrator |
DOI | 10.1109/SIBGRAPI54419.2021.00018 |
Citation Key | EscherDrewBem:2021:FaSpTr |
Title | Fast Spatial-Temporal Transformer Network |
Format | On-line |
Year | 2021 |
Access Date | 2024, May 06 |
Number of Files | 1 |
Size | 10314 KiB |
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2. Context | |
Author | 1 Escher, Rafael Molossi 2 Drews-Jr, Paulo 3 Bem, Rodrigo Andrade de |
Affiliation | 1 Federal University of Rio Grande 2 Federal University of Rio Grande 3 Federal University of Rio Grande |
Editor | Paiva, 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 Address | rafael-escher@hotmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 34 (SIBGRAPI) |
Conference Location | Gramado, RS, Brazil (virtual) |
Date | 18-22 Oct. 2021 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2021-09-06 22:26:16 :: rafael-escher@hotmail.com -> administrator :: 2022-03-02 00:54:16 :: administrator -> menottid@gmail.com :: 2021 2022-03-02 13:24:39 :: menottid@gmail.com -> administrator :: 2021 2022-06-14 00:00:32 :: administrator -> :: 2021 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Deep Learning Video Inpainting Reformer Networks Transformer Networks |
Abstract | In computer vision, the restoration of missing regions in an image can be tackled with image inpainting techniques. Neural networks that perform inpainting in videos require the extraction of information from neighboring frames to obtain a temporally coherent result. The state-of-the-art methods for video inpainting are mainly based on Transformer Networks, which rely on attention mechanisms to handle temporal input data. However, such networks are highly costly, requiring considerable computational power for training and testing, which hinders its use on modest computing platforms. In this context, our goal is to reduce the computational complexity of state-ofthe-art video inpainting methods, improving performance and facilitating its use in low-end GPUs. Therefore, we introduce the Fast Spatio-Temporal Transformer Network (FastSTTN), an extension of the Spatio-Temporal Transformer Network (STTN) in which the adoption of Reversible Layers reduces memory usage up to 7 times and execution time by approximately 2.2 times, while maintaining state-of-the-art video inpainting accuracy. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Fast Spatial-Temporal Transformer... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Fast Spatial-Temporal Transformer... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/45CUSQ5 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/45CUSQ5 |
Language | en |
Target File | FastSTTN___SIBGRAPI_2021.pdf |
User Group | rafael-escher@hotmail.com |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/45PQ3RS 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2021/11.12.11.46 5 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy 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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