Search Results for author: Thiago Oliveira-Santos

Found 23 papers, 13 papers with code

Deep Learning-based Type Identification of Volumetric MRI Sequences

1 code implementation6 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.

Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?

1 code implementation21 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.

Deep traffic light detection by overlaying synthetic context on arbitrary natural images

1 code implementation7 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.

Autonomous Driving

Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection

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.

Lane Detection

What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment

no code implementations19 Sep 2020 Filipe Mutz, Thiago Oliveira-Santos, Avelino Forechi, Karin S. Komati, Claudine Badue, Felipe M. G. França, Alberto F. de Souza

In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps.

Self-Driving Cars

Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents

1 code implementation1 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.

Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning

1 code implementation23 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.

Metric Learning

Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks

no code implementations2 Oct 2019 Pedro Azevedo, Sabrina S. Panceri, Rânik Guidolini, Vinicius B. Cardoso, Claudine Badue, Thiago Oliveira-Santos, Alberto F. de Souza

We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car.

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

1 code implementation19 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.

Autonomous Vehicles object-detection +3

Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars

1 code implementation4 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.

Budget-Aware Adapters for Multi-Domain Learning

no code implementations ICCV 2019 Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe, Elisa Ricci

To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.

Ego-Lane Analysis System (ELAS): Dataset and Algorithms

no code implementations15 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).

Change Detection General Classification +1

Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data

1 code implementation14 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.

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

no code implementations27 Apr 2018 Raphael V. Carneiro, Rafael C. Nascimento, Rânik Guidolini, Vinicius B. Cardoso, Thiago Oliveira-Santos, Claudine Badue, Alberto F. de Souza

We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations.

Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification

1 code implementation28 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.

Classification General Classification

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