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Quentin Bouniot
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2020 – today
- 2024
- [i9]Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc:
Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR abs/2407.01331 (2024) - 2023
- [b1]Quentin Bouniot:
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). Jean Monnet University, Saint-Étienne, France, 2023 - [c6]Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo
, Hanlin Tian, Kenta Matsui, Tianhao Wang, Fahmy Adan, Zhitong Gao, Xuming He, Quentin Bouniot
The Robust Semantic Segmentation UNCV2023 Challenge Results. ICCV (Workshops) 2023: 4620-4630 - [c5]Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Proposal-Contrastive Pretraining for Object Detection from Fewer Data. ICLR 2023 - [c4]Quentin Bouniot
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient? WACV 2023: 75-84 - [i8]Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo
The Robust Semantic Segmentation UNCV2023 Challenge Results. CoRR abs/2309.15478 (2023) - [i7]Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski:
Understanding deep neural networks through the lens of their non-linearity. CoRR abs/2310.11439 (2023) - [i6]Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Proposal-Contrastive Pretraining for Object Detection from Fewer Data. CoRR abs/2310.16835 (2023) - [i5]Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard:
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ? CoRR abs/2310.19936 (2023) - [i4]Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc:
Tailoring Mixup to Data using Kernel Warping functions. CoRR abs/2311.01434 (2023) - [i3]Quentin Bouniot:
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks. CoRR abs/2311.04888 (2023) - 2022
- [c3]Quentin Bouniot
Improving Few-Shot Learning Through Multi-task Representation Learning Theory. ECCV (20) 2022: 435-452 - 2021
- [i2]Quentin Bouniot, Romaric Audigier, Angélique Loesch:
Optimal Transport as a Defense Against Adversarial Attacks. CoRR abs/2102.03156 (2021) - 2020
- [c2]Quentin Bouniot
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks. CVPR Workshops 2020: 3450-3459 - [c1]Quentin Bouniot
Optimal Transport as a Defense Against Adversarial Attacks. ICPR 2020: 5044-5051 - [i1]Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms. CoRR abs/2010.01992 (2020)
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