Speaker
Sébastien Gerchinovitz
(IRT Saint Exupéry)
Description
We address the problem of constructing reliable uncertainty estimates for object detection. We build upon classical tools from Conformal Prediction, which offer (marginal) risk guarantees when the predictive uncertainty can be reduced to a one-dimensional parameter. In this talk, we will first recall standard algorithms and theoretical guarantees in conformal prediction and beyond. We will then address the problem of tuning a two-dimensional uncertainty parameter, and will illustrate our method on an objection detection task. This is a joint work with Léo Andéol, Luca Mossina, and Adrien Mazoyer.
