Sylvie Le Hégarat-Mascle — accepted AI-conference papers

2026

Fisher-Rao Sensitivity for Out-of-Distribution Detection in Deep Neural Networks

Anthony Nguyen, Antonin Bertrand, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Franck FLORIN, Mohammed Nabil EL-KORSO +1

ICLR 2026poster

Deep neural networks often remain overconfident on Out-of-Distribution (OoD) inputs. We revisit this problem through Riemannian information geometry. We model the network's predictions as a statistical manifold and find that OoD inputs exhibit higher local Fisher-Rao sensitivity. By quantifying this…

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2026

SE(n)-Invariant Flow Matching: A General Framework with Application to Object Reassembly

Gaël Heck, Sylvie Le Hégarat-Mascle, Nicolas Lermé

ICML 2026poster

Reassembling $N$ fragments in $n$-dimensional space is a shape reconstruction task that is invariant to global rigid motions. Training directly on $\mathcal{M}=\mathrm{SE}(n)^N$ can be ill-posed: standard losses penalize solutions that differ only by a global transform. Existing methods often addres…

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2025

Stochastic Embeddings : A Probabilistic and Geometric Analysis of Out-of-Distribution Behavior

Anthony Nguyen, Emanuel Aldea, Sylvie Le Hégarat-Mascle, Renaud Lustrat

UAI 2025

Deep neural networks perform well in many applications but often fail when exposed to out-of-distribution (OoD) inputs. We identify a geometric phenomenon in the embedding space: in-distribution (ID) data show higher variance than OoD data under stochastic perturbations. Using high-dimensional geome

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2024

A Contrario Paradigm for Yolo-Based Infrared Small Target Detection

Alina Ciocarlan, Sylvie Le Hégarat-Mascle, Sidonie Lefebvre, Arnaud Woiselle, Clara Barbanson

ICASSP 2024accepted

Detecting small to tiny targets in infrared images is a challenging task in computer vision, especially when it comes to differentiating these targets from noisy or textured backgrounds. Traditional object detection methods such as YOLO struggle to detect tiny objects compared to segmentation neural…

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