69 citations · 304 across the 14 of their papers we have counts for
14 papers
Multi-Cue Adaptive Emotion Recognition Network
Willams Costa, David Macêdo, Cleber Zanchettin +2
Expressing and identifying emotions through facial and physical expressions is a significant part of social interaction. Emotion recognition is an essential task in computer vision…
Training Aware Sigmoidal Optimizer
David Macêdo, Pedro Dreyer, Teresa Ludermir +1
Proper optimization of deep neural networks is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defin…
KutralNet: A Portable Deep Learning Model for Fire Recognition
Angel Ayala, Bruno Fernandes, Francisco Cruz +3
Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to pe…
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to pr…
A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
Ricardo Batista das Neves Junior, Luiz Felipe Verçosa, David Macêdo +2
The Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide practices to online customer identification based on personal identification documents, similarity and li…
Distantly-Supervised Neural Relation Extraction with Side Information using BERT
Johny Moreira, Chaina Oliveira, David Macêdo +2
Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), whi…
AM-MobileNet1D: A Portable Model for Speaker Recognition
João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin
Speaker Recognition and Speaker Identification are challenging tasks with essential applications such as automation, authentication, and security. Deep learning approaches like Sin…
Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
Heitor Felix, Walber M. Rodrigues, David Macêdo +4
The detection of objects considering a 6DoF pose is a common requirement to build virtual and augmented reality applications. It is usually a complex task which requires real-time…
Entropic Out-of-Distribution Detection
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., cl…
Additive Margin SincNet for Speaker Recognition
João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin
Speaker Recognition is a challenging task with essential applications such as authentication, automation, and security. The SincNet is a new deep learning based model which has pro…
Heartbeat Anomaly Detection using Adversarial Oversampling
Jefferson L. P. Lima, David Macêdo, Cleber Zanchettin
Cardiovascular diseases are one of the most common causes of death in the world. Prevention, knowledge of previous cases in the family, and early detection is the best strategy to…
Squeezed Very Deep Convolutional Neural Networks for Text Classification
Andréa B. Duque, Luã Lázaro J. Santos, David Macêdo +1
Most of the research in convolutional neural networks has focused on increasing network depth to improve accuracy, resulting in a massive number of parameters which restricts the t…
Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition
Cleison Correia de Amorim, David Macêdo, Cleber Zanchettin
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Tempo…
Hierarchical Attentional Hybrid Neural Networks for Document Classification
Jader Abreu, Luis Fred, David Macêdo +1
Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, th…