111 citations · 260 across the 8 of their papers we have counts for
12 papers
Cross-Domain Object Detection Using Unsupervised Image Translation
Vinicius F. Arruda, Rodrigo F. Berriel, Thiago M. Paixão +4
Unsupervised domain adaptation for object detection addresses the adaption of detectors trained in a source domain to work accurately in an unseen target domain. Recently, methods…
Deep Learning-based Type Identification of Volumetric MRI Sequences
Jean Pablo Vieira de Mello, Thiago M. Paixão, Rodrigo Berriel +4
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume…
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
Jean Pablo Vieira de Mello, Lucas Tabelini, Rodrigo F. Berriel +5
Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the mod…
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +3
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 a…
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +4
Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. Th…
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres +4
The reconstruction of shredded documents consists of coherently arranging fragments of paper (shreds) to recover the original document(s). A great challenge in computational recons…
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres +4
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents. This task is particularly…
PolyLaneNet: Lane Estimation via Deep Polynomial Regression
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +3
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has y…
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Lucas Tabelini Torres, Thiago M. Paixão, Rodrigo F. Berriel +4
Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image sa…
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Vinicius F. Arruda, Thiago M. Paixão, Rodrigo F. Berriel +4
Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy…
Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars
Lucas C. Possatti, Rânik Guidolini, Vinicius B. Cardoso +5
Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing their current states to share the streets with human drivers. Most of the time, human d…
Self-Driving Cars: A Survey
Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro +10
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system tha…