Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks. - Quentin Bouniot; Romaric Audigier; Angélique Loesch (CVPR Workshops, 2020)

Optimal Transport as a Defense Against Adversarial Attacks. - Quentin Bouniot; Romaric Audigier; Angelique Loesch (ICPR, 2020)

Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms. - Quentin Bouniot; Ievgen Redko; Romaric Audigier; Angélique Loesch; Amaury Habrard (CoRR, 2020)

Improving Few-Shot Learning Through Multi-task Representation Learning Theory. - Quentin Bouniot; Ievgen Redko; Romaric Audigier; Angélique Loesch; Amaury Habrard (ECCV (20), 2022)

The Robust Semantic Segmentation UNCV2023 Challenge Results. - Xuanlong Yu; Yi Zuo; Zitao Wang; Xiaowen Zhang; Jiaxuan Zhao; Yuting Yang 008; Licheng Jiao; Rui Peng; Xinyi Wang; Junpei Zhang; Kexin Zhang 003; Fang Liu 001; et al. (ICCV (Workshops), 2023)

Proposal-Contrastive Pretraining for Object Detection from Fewer Data. - Quentin Bouniot; Romaric Audigier; Angélique Loesch; Amaury Habrard (ICLR, 2023)

Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient? - Quentin Bouniot; Angélique Loesch; Amaury Habrard; Romaric Audigier (WACV, 2023)

Understanding deep neural networks through the lens of their non-linearity. - Quentin Bouniot; Ievgen Redko; Anton Mallasto; Charlotte Laclau; Karol Arndt; Oliver Struckmeier; Markus Heinonen; Ville Kyrki; Samuel Kaski (CoRR, 2023)

Tailoring Mixup to Data using Kernel Warping functions. - Quentin Bouniot; Pavlo Mozharovskyi; Florence d'Alché-Buc (CoRR, 2023)

Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks. - Quentin Bouniot (CoRR, 2023)

Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks. (Vers un Apprentissage avec peu d'annotations en Vision par Ordinateur : Application aux tâches de classification d'images et de détection d'objets). - Quentin Bouniot (2023)

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)

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. - Jayneel Parekh; Quentin Bouniot; Pavlo Mozharovskyi; Alasdair Newson; Florence d'Alché-Buc (ICLR, 2025)

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models. - Mateusz Pach; Shyamgopal Karthik; Quentin Bouniot; Serge J. Belongie; Zeynep Akata (CoRR, 2025)

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers. - Simon Roschmann; Quentin Bouniot; Vasilii Feofanov; Ievgen Redko; Zeynep Akata (CoRR, 2025)