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Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks.

Quentin Bouniot, Romaric Audigier, Angélique Loesch

CVPRW, 2020

Optimal Transport as a Defense Against Adversarial Attacks

Quentin Bouniot, Romaric Audigier, Angélique Loesch

ICPR, 2021

Improving Few-Shot Learning through Multi-task Representation Learning Theory

Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard

ECCV, 2022

Towards Few-Annotation Learning for Object Detection: Are Transformer-Based Models More Efficient?

Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard

WACV, 2023

Proposal-Contrastive Pretraining for Object Detection from Fewer Data

Quentin Bouniot, Romaric Audigier, Angelique Loesch, Amaury Habrard

ICLR, 2023

Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks

Quentin Bouniot

PhD Thesis, 2023

Tailoring Mixup to Data for Calibration

Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc

ICLR, 2025

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Permalink

Jayneel Parekh*, Quentin Bouniot*, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc

ICLR, 2025

From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport

Quentin Bouniot*, Ievgen Redko*, Anton Mallasto, Charlotte Laclau, Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski

CVPR, 2025

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge Belongie, Zeynep Akata

NeurIPS, 2025

Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks

Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks

Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms

Optimal Transport as a Defense Against Adversarial Attacks

Vers une meilleure compréhension des méthodes de méta-apprentissage à travers la théorie de l’apprentissage de représentations multi-tâches

Improving Few-Shot Learning through Multi-task Representation Learning Theory

On Few-Annotation Learning and Non-Linearity in Deep Neural Networks

Understanding Deep Neural Networks Through the Lens of their Non-Linearity

Algorithms and complexity

Teaching Assistant, CentraleSupélec, Université Paris-Saclay, 1900

Recent Advances in Responsible AI

Course Lecturer, Télécom Paris, Institut Polytechnique de Paris, 1900

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