1 citations · 1 across the 5 of their papers we have counts for
6 papers
GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
Diogo Lavado, Alessandra Micheletti, Clàudia Soares
In 3D scene understanding, deep learning models rely on large models and extensive training to capture basic geometric structures that are present in the 3D data. However, existing…
Enhancing Power Grid Inspections with Machine Learning
Diogo Lavado, Ricardo Santos, Andre Coelho +3
Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter…
TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System
Diogo Lavado, Cláudia Soares, Alessandra Micheletti +3
Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force o…
Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach
Diogo Lavado, Cláudia Soares, Alessandra Micheletti
Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting. Fixed penalty methods, though common, lack adaptability and suffer…
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification
Diogo Lavado, Cláudia Soares, Alessandra Micheletti +4
Research in 3D semantic segmentation has been increasing performance metrics, like the IoU, by scaling model complexity and computational resources, leaving behind researchers and…
Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks
João Ferreira, Diogo Lavado, Ricardo Gonçalves +3
With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to…