9 citations · 14 across the 4 of their papers we have counts for
6 papers · 1 filter
Learning a Discriminant Latent Space with Neural Discriminant Analysis
Mai Lan Ha, Gianni Franchi, Emanuel Aldea +1
Discriminative features play an important role in image and object classification and also in other fields of research such as semi-supervised learning, fine-grained classification…
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. Whi…
One Versus all for deep Neural Network Incertitude (OVNNI) quantification
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
Deep neural networks (DNNs) are powerful learning models yet their results are not always reliable. This is due to the fact that modern DNNs are usually uncalibrated and we cannot…
Evaluating Crowd Density Estimators via Their Uncertainty Bounds
Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hégarat-Mascle
In this work, we use the Belief Function Theory which extends the probabilistic framework in order to provide uncertainty bounds to different categories of crowd density estimators…
Geometry-Based Multiple Camera Head Detection in Dense Crowds
Nicola Pellicanò, Emanuel Aldea, Sylvie Le Hégarat-Mascle
This paper addresses the problem of head detection in crowded environments. Our detection is based entirely on the geometric consistency across cameras with overlapping fields of v…
Efficient Evaluation of the Number of False Alarm Criterion
Sylvie Le Hégarat-Mascle, Emanuel Aldea, Jennifer Vandoni
This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involv…