13 citations · 41 across the 15 of their papers we have counts for
16 papers
ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)
Sofia Marino, Jennifer Vandoni, Emanuel Aldea +3
In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we pr…
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Xuanlong Yu, Gianni Franchi, Jindong Gu +1
Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE) is one of the most effective…
Latent Discriminant deterministic Uncertainty
Gianni Franchi, Xuanlong Yu, Andrei Bursuc +3
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensiv…
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks
Gianni Franchi, Xuanlong Yu, Andrei Bursuc +5
Predictive uncertainty estimation is essential for safe deployment of Deep Neural Networks in real-world autonomous systems. However, disentangling the different types and sources…
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
Xuanlong Yu, Gianni Franchi, Emanuel Aldea
Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set o…
SLURP: Side Learning Uncertainty for Regression Problems
Xuanlong Yu, Gianni Franchi, Emanuel Aldea
It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results. Uncertainty estimation f…
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…
TRADI: Tracking deep neural network weight distributions for uncertainty estimation
Gianni Franchi, Andrei Bursuc, Emanuel Aldea +2
During training, the weights of a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss function. Only this final st…
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…
2CoBel : An Efficient Belief Function Extension for Two-dimensional Continuous Spaces
Nicola Pellicanò, Sylvie Le Hégarat-Mascle, Emanuel Aldea
This paper introduces an innovative approach for handling 2D compound hypotheses within the Belief Function Theory framework. We propose a polygon-based generic rep- resentation wh…
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
Emanuel Aldea, Khurom H. Kiyani
In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based method…
SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion
Nadege Zarrouati, Emanuel Aldea, Pierre Rouchon
In this paper, we use known camera motion associated to a video sequence of a static scene in order to estimate and incrementally refine the surrounding depth field. We exploit the…