Showing cs.CVShow all
3 papers · 1 filter
cs.CV2023
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
Pedro Conde, Rui L. Lopes, Cristiano Premebida
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a g…
cs.CV2023
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
Pedro Conde, Tiago Barros, Rui L. Lopes +2
With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. Thi…
cs.CV2023
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
T. Barros, L. Garrote, P. Conde +4
Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to st…