46 citations · 77 across the 9 of their papers we have counts for
9 papers
CCNeXt: An Effective Self-Supervised Stereo Depth Estimation Approach
Alexandre Lopes, Roberto Souza, Helio Pedrini
Depth Estimation plays a crucial role in recent applications in robotics, autonomous vehicles, and augmented reality. These scenarios commonly operate under constraints imposed by…
Computer Vision Model Compression Techniques for Embedded Systems: A Survey
Alexandre Lopes, Fernando Pereira dos Santos, Diulhio de Oliveira +2
Deep neural networks have consistently represented the state of the art in most computer vision problems. In these scenarios, larger and more complex models have demonstrated super…
P-NOC: adversarial training of CAM generating networks for robust weakly supervised semantic segmentation priors
Lucas David, Helio Pedrini, Zanoni Dias
Weakly Supervised Semantic Segmentation (WSSS) techniques explore individual regularization strategies to refine Class Activation Maps (CAMs). In this work, we first analyze comple…
MIA-3DCNN: COVID-19 Detection Based on a 3D CNN
Igor Kenzo Ishikawa Oshiro Nakashima, Giovanna Vendramini, Helio Pedrini
Early and accurate diagnosis of COVID-19 is essential to control the rapid spread of the pandemic and mitigate sequelae in the population. Current diagnostic methods, such as RT-PC…
When Layers Play the Lottery, all Tickets Win at Initialization
Artur Jordao, George Correa de Araujo, Helena de Almeida Maia +1
Pruning is a standard technique for reducing the computational cost of deep networks. Many advances in pruning leverage concepts from the Lottery Ticket Hypothesis (LTH). LTH revea…
Global Semantic Descriptors for Zero-Shot Action Recognition
Valter Estevam, Rayson Laroca, Helio Pedrini +1
The success of Zero-shot Action Recognition (ZSAR) methods is intrinsically related to the nature of semantic side information used to transfer knowledge, although this aspect has…
A Survey on RGB-D Datasets
Alexandre Lopes, Roberto Souza, Helio Pedrini
RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and med…
Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos +20
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific communi…
Lite Training Strategies for Portuguese-English and English-Portuguese Translation
Alexandre Lopes, Rodrigo Nogueira, Roberto Lotufo +1
Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models. In this work, we investigate the use of…