1 code implementation • 21 Jan 2021 • Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images.
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.
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 • 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.
no code implementations • 15 Jun 2018 • Rodrigo F. Berriel, Edilson de Aguiar, Alberto F. de Souza, Thiago Oliveira-Santos
The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i. e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
1 code implementation • 14 Jun 2018 • Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network.
no code implementations • 30 May 2018 • Rodrigo F. Berriel, Franco Schmidt Rossi, Alberto F. de Souza, Thiago Oliveira-Santos
Many crosswalk classification, detection and localization systems have been proposed in the literature over the years.
1 code implementation • 28 Jun 2017 • Rodrigo F. Berriel, Andre Teixeira Lopes, Alberto F. de Souza, Thiago Oliveira-Santos
In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks.