1. Identificação | |
Tipo de Referência | Artigo em Evento (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Código do Detentor | ibi 8JMKD3MGPEW34M/46T9EHH |
Identificador | 8JMKD3MGPEW34M/43B8A7P |
Repositório | sid.inpe.br/sibgrapi/2020/09.28.22.29 |
Última Atualização | 2020:09.28.22.29.43 (UTC) administrator |
Repositório de Metadados | sid.inpe.br/sibgrapi/2020/09.28.22.29.43 |
Última Atualização dos Metadados | 2022:06.14.00.00.10 (UTC) administrator |
DOI | 10.1109/SIBGRAPI51738.2020.00051 |
Chave de Citação | Schirmer:2020:Li2DPo |
Título | A lightweight 2D Pose Machine with attention enhancement |
Formato | On-line |
Ano | 2020 |
Data de Acesso | 17 maio 2024 |
Número de Arquivos | 1 |
Tamanho | 7396 KiB |
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2. Contextualização | |
Autor | Schirmer, Luiz |
Afiliação | PUC-rio |
Editor | Musse, Soraia Raupp Cesar Junior, Roberto Marcondes Pelechano, Nuria Wang, Zhangyang (Atlas) |
Endereço de e-Mail | schirmer.luizj@gmail.com |
Nome do Evento | Conference on Graphics, Patterns and Images, 33 (SIBGRAPI) |
Localização do Evento | Porto de Galinhas (virtual) |
Data | 7-10 Nov. 2020 |
Editora (Publisher) | IEEE Computer Society |
Cidade da Editora | Los Alamitos |
Título do Livro | Proceedings |
Tipo Terciário | Full Paper |
Histórico (UTC) | 2020-09-28 22:29:43 :: schirmer.luizj@gmail.com -> administrator :: 2022-06-14 00:00:10 :: administrator -> schirmer.luizj@gmail.com :: 2020 |
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3. Conteúdo e estrutura | |
É a matriz ou uma cópia? | é a matriz |
Estágio do Conteúdo | concluido |
Transferível | 1 |
Tipo de Versão | finaldraft |
Palavras-Chave | pose estimation tensor decompostion attention layer |
Resumo | Pose estimation is a challenging task in computer vision that has many applications, as for example: in motion capture, in medical analysis, in human posture monitoring, and in robotics. In other words, it is a main tool to enable machines do understand human patterns in videos or images. Performing this task in real-time while maintaining accuracy and precision is critical for many of these applications. Several papers propose real time approaches considering deep neural networks for pose estimation. However, in most cases they fail when considering run-time performance or do not achieve the precision needed. In this work, we propose a new model for real-time pose estimation considering attention modules for convolutional neural networks (CNNs). We introduce a two-dimensional relative attention mechanism for feature extraction in pose machines leading to improvements in accuracy. We create a single shot architecture where both operations to infer keypoints and part affinity fields share the information. Also, for each stage, we use tensor decompositions to not only reduce dimensionality, but also to improve performance. This allows us to factorize each convolution and drastically reduce the number of parameters in our network. Our experiments show that, with this factorized approach, it is possible to achieve state-of-art performance in terms of run-time while we have a small reduction in accuracy. |
Arranjo 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2020 > A lightweight 2D... |
Arranjo 2 | urlib.net > SDLA > Fonds > Full Index > A lightweight 2D... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
URL dos dados | http://urlib.net/ibi/8JMKD3MGPEW34M/43B8A7P |
URL dos dados zipados | http://urlib.net/zip/8JMKD3MGPEW34M/43B8A7P |
Idioma | en |
Arquivo Alvo | Pose_estimation_for_Sibgrapi_2020.pdf |
Grupo de Usuários | schirmer.luizj@gmail.com |
Visibilidade | shown |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Repositório Espelho | sid.inpe.br/banon/2001/03.30.15.38.24 |
Unidades Imediatamente Superiores | 8JMKD3MGPEW34M/43G4L9S 8JMKD3MGPEW34M/4742MCS |
Lista de Itens Citando | sid.inpe.br/sibgrapi/2020/10.28.20.46 11 |
Acervo Hospedeiro | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notas | |
Campos Vazios | 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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7. Controle da descrição | |
e-Mail (login) | schirmer.luizj@gmail.com |
atualizar | |
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