Publications

Do you want to know more about improving the transferability of a ViT for unsupervised domain adaptation? Check our paper “Transferable-guided Attention Is All You Need for Video Domain Adaptation”, just accepted for publication in the IEEE/CVF Winter Conference on Applications of Computer Vision.

Are you looking for a robust dataset for real-time classification of leaf diseases using thermal imaging? Check our paper “An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging”, just published in the IEEE Geoscience and Remote Sensing Letters.

Do you want to know more about fine-tuning SAM for lung segmentation in chest X-ray images? Check our paper “Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images”, just accepted for publication in the Iberoamerican Congress on Pattern Recognition.

Do you want to know more about finding the most effective neural network architectures for the compressed domain? Check our paper “Action Recognition in the Compressed Domain: A Neural Search Approach”, just accepted for publication in the International Conference on Systems, Signals and Image Processing.

Do you want to know more about augmenting data for training intelligent systems to decipher dysarthric speech? Check our paper “Exploring Alternative Data Augmentation Methods in Dysarthric Automatic Speech Recognition”, just accepted for publication in the IEEE International Symposium on Computer-Based Medical Systems.

Are you looking for a robust loss function for forecasting signal quality indicators in 5G networks? Check our paper “Residual-based Adaptive Huber Loss (RAHL) - Design of an Improved Huber Loss for CQI Prediction in 5G Networks”, just accepted for publication in the Brazilian Symposium on Computer Networks and Distributed Systems.

Do you want to know more about selecting and combining classifiers? Check our paper “Strategies for Classifier Selection Based on Genetic Programming for Multimedia Data Recognition”, just accepted for publication in the International Conference on Computer Vision Theory and Applications.

Do you want to know more about using unknown samples to tighten the classification boundaries? Check our paper “Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation”, just accepted for publication in the SIBGRAPI - Conference on Graphics, Patterns and Images.

Are you looking for a robust dataset for training highly accurate prediction models to automatically detect productive crop fields? Check our paper “Productive Crop Field Detection: A New Dataset and Deep-Learning Benchmark Results”, just published in the IEEE Geoscience and Remote Sensing Letters.

Do you want to know more about pruning models for multiple domains according to a user budget? Check our paper “Budget-Aware Pruning for Multi-Domain Learning”, just accepted for publication in the International Conference on Image Analysis and Processing.