Thiago Oliveira-Santos — accepted AI-conference papers

2025

Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions

Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos

COLING 2025main

Multiple choice questions (MCQs) are often used in both employee selection and training, providing objectivity, efficiency, and scalability. However, their creation is resource-intensive, requiring significant expertise and financial investment. This study leverages large language models (LLMs) and…

Cited by 2 Source ↗PDF ↗Scholar ↗

2021

Keep Your Eyes on the Lane: Real-Time Attention-Guided Lane Detection

Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixao, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos

CVPR 2021poster

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. In this work, we propose LaneATT: an anchor-based deep lane detection model, which, akin to other ge…

Cited by 482 PDF ↗code ↗Scholar ↗

2020

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

Thiago M. Paixao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. De Souza +1

CVPR 2020poster

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 relevant for supporting forensic investigation as documents may contain criminal evidence. As an alternative to the labo…

Cited by 13 PDF ↗code ↗Scholar ↗

2019

Budget-Aware Adapters for Multi-Domain Learning

Rodrigo Berriel, Stephane Lathuillere, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe +1

ICCV 2019poster

Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL with a particular interest in obtaining domain-specific models…

Cited by 45 PDF ↗Scholar ↗

2017

A Model-Predictive Motion Planner for the IARA autonomous car

Vinicius Cardoso, Josias Oliveira, Thomas Teixeira, Claudine Badue, Filipe Mutz, Thiago Oliveira-Santos +2

ICRA 2017poster

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and…

Cited by 50 Source ↗Scholar ↗

2015

Image-based mapping, global localization and position tracking using VG-RAM weightless neural networks

Lauro J. Lyrio, Thiago Oliveira-Santos, Claudine Badue, Alberto Ferreira De Souza

ICRA 2015poster

Humans can easily memorize images of places and labels (road names, addresses, etc.) associated with them, as well as trajectories defined by sequences of images and corresponding positions. Later, they are able to remember places' labels and relative positions when seeing the same images again. In…

Cited by 21 Source ↗Scholar ↗

2015

Re-emission and satellite aerial maps applied to vehicle localization on urban environments

Lucas De Paula Veronese, Edilson de Aguiar, Rafael Correia Nascimento, Jose Guivant, Fernando A. Auat Cheein, Alberto Ferreira De Souza +1

IROS 2015poster

Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle…

Cited by 43 Source ↗Scholar ↗