19 citations · 52 across the 7 of their papers we have counts for
17 papers
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…
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?
Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue +2
Convolutional neural networks have been successful lately enabling companies to develop neural-based products, which demand an expensive process, involving data acquisition and ann…
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…
What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment
Filipe Mutz, Thiago Oliveira-Santos, Avelino Forechi +4
The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps…
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…