(DSc Thesis) Luiz Schirmer
Channel: Departamento de Informática - PUC-Rio
Date: 23/04/2021

Title: Semantic graph attention networks and tensor decompositions for computer vision and computer graphics

Advisors: Hélio Lopes
                  Luiz Velho (IMPA)

Abstract: 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 pose estimation. Pose estimation is a challenging problem in computer vision with many real applications in areas including augmented reality, virtual reality, computer animation, and 3D scene reconstruction. Usually, the problem to be addressed involves estimating the 2D and 3D human pose, i.e., the anatomical keypoints or body “parts” of persons in images or videos. Several papers propose approaches to achieve high accuracy using architectures based on conventional convolution neural networks; however, mistakes caused by occlusion and motion blur are not uncommon, and those models are computationally very intensive for real-time applications. We explore different architectures to improve processing time, 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.

Publicações relacionadas: 

Silva, L. J. S., da Silva, D. L. S., Raposo, A. B., Velho, L., & Lopes, H. C. V. (2019). Tensorpose: Real-time pose estimation for interactive applications. Computers & Graphics, 85, 1-14.

Silva, L. J. S., da Silva, D. S., Velho, L., & Lopes, H. (2020). An end-to-end framework for 3D capture and human digitization with a single RGB camera. Eurographics conference proceedings.

Schirmer, L., Lucio, D., Raposo, A., Velho, L., & Lopes, H. (2020, November). A lightweight 2D Pose Machine with attention enhancement. In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 324-331). IEEE.

Referência da tese:
Schirmer, L. (2021, February). Semantic graph attention networks and tensor decompositions for computer vision and computer graphics. Ph.D. Thesis, Pontifical Catholic University of Rio de Janeiro.