%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.13.20.01
%2 sid.inpe.br/sibgrapi/2021/09.13.20.01.21
%T Semantic graph attention networks and tensor decompositions for computer vision and computer graphics
%D 2021
%A Schirmer, Luiz,
%A Velho, Luiz,
%A Lopes, Hélio,
%@affiliation PUC-Rio
%@affiliation IMPA
%@affiliation PUC-Rio
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K Neural Networks, Gaph Neural Networks, Human Pose Estimation.
%X This thesis proposes new architectures for deep neural networks with attention enhancement and multilinear algebra methods to increase their performance. We also explore graph convolutions and their particularities. We focus here on the problems related to real-time human pose estimation. We explore different architectures to reduce computational complexity, and, as a result, we propose two novel neural network models for 2D and 3D pose estimation. We also introduce a new architecture for Graph attention networks called Semantic Graph Attention.
%@language en
%3 WTD_Sibgrapi_2021 (4).pdf