no code implementations • 29 Apr 2024 • Carlos Eduardo G. R. Alves, Francisco de Assis Boldt, Thiago M. Paixão
In particular, there is the problem of recognizing isolated signs (Isolated Sign Language Recognition, ISLR) of great relevance in the development of vision-based SL search engines, learning tools, and translation systems.
Ranked #1 on Sign Language Recognition on LIBRAS-UFOP
1 code implementation • 6 Jun 2021 • Jean Pablo Vieira de Mello, Thiago M. Paixão, Rodrigo Berriel, Mauricio Reyes, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor.
1 code implementation • 7 Nov 2020 • Jean Pablo Vieira de Mello, Lucas Tabelini, Rodrigo F. Berriel, Thiago M. Paixão, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
2 code implementations • CVPR 2021 • Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.
Ranked #6 on Lane Detection on LLAMAS
no code implementations • 30 Jul 2020 • Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
1 code implementation • 1 Jul 2020 • Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
1 code implementation • arXiv 2020 • Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
Ranked #9 on Lane Detection on LLAMAS
1 code implementation • 23 Mar 2020 • Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres, Alessando L. Koerich, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents.
1 code implementation • 23 Jul 2019 • Lucas Tabelini Torres, Thiago M. Paixão, Rodrigo F. Berriel, Alberto F. de Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
Deep learning has been successfully applied to several problems related to autonomous driving.
1 code implementation • 19 Jul 2019 • Vinicius F. Arruda, Thiago M. Paixão, Rodrigo F. Berriel, Alberto F. De Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
1 code implementation • 4 Jun 2019 • Lucas C. Possatti, Rânik Guidolini, Vinicius B. Cardoso, Rodrigo F. Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights.