Quentin Bouniot

27 papers A* 4A 1B 1Journal 17Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers. CoRR Yiran Huang, Karsten Roth, Quentin Bouniot, Wenjia Xu, Zeynep Akata
2026	J	`jnl`	Rethinking Concept Bottleneck Models: From Pitfalls to Solutions. CoRR Merve Tapli, Quentin Bouniot, Wolfgang Stammer, Zeynep Akata, Emre Akbas
2026	J	`jnl`	SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport. CoRR Simon Roschmann, Paul Krzakala, Sonia Mazelet, Quentin Bouniot, Zeynep Akata
2026	J	`jnl`	The Latent Color Subspace: Emergent Order in High-Dimensional Chaos. CoRR Mateusz Pach, Jessica Bader, Quentin Bouniot, Serge J. Belongie, Zeynep Akata
2026	J	`jnl`	TimeSAE: Sparse Decoding for Faithful Explanations of Black-Box Time Series Models. CoRR Khalid Oublal, Quentin Bouniot, Qi Gan, Stéphan Clémençon, Zeynep Akata
2026	J	`jnl`	UNBOX: Unveiling Black-box visual models with Natural-language. CoRR Simone Carnemolla, Chiara Maria Russo, Simone Palazzo, Quentin Bouniot, Daniela Giordano, Zeynep Akata, Matteo Pennisi, Concetto Spampinato
2025	A*	`conf`	From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport. CVPR Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski
2025	A*	`conf`	Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. ICLR Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
2025	J	`jnl`	Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models. CoRR Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge J. Belongie, Zeynep Akata
2025	A*	`conf`	Tailoring Mixup to Data for Calibration. ICLR Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
2025	J	`jnl`	Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers. CoRR Simon Roschmann, Quentin Bouniot, Vasilii Feofanov, Ievgen Redko, Zeynep Akata
2024	J	`jnl`	Restyling Unsupervised Concept Based Interpretable Networks with Generative Models. CoRR Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
2023	A*	`conf`	Proposal-Contrastive Pretraining for Object Detection from Fewer Data. ICLR Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard
2023	J	`jnl`	Proposal-Contrastive Pretraining for Object Detection from Fewer Data. CoRR Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard
2023	J	`jnl`	Tailoring Mixup to Data using Kernel Warping functions. CoRR Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
2023	—	`conf`	The Robust Semantic Segmentation UNCV2023 Challenge Results. ICCV (Workshops) 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, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill D. F. Campbell, Gianni Franchi
2023	J	`jnl`	The Robust Semantic Segmentation UNCV2023 Challenge Results. CoRR 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, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill D. F. Campbell, Gianni Franchi
2023	J	`jnl`	Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ? CoRR Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard
2023	A	`conf`	Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient? WACV Quentin Bouniot, Angélique Loesch, Amaury Habrard, Romaric Audigier
2023	J	`jnl`	Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks. CoRR Quentin Bouniot
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	J	`jnl`	Understanding deep neural networks through the lens of their non-linearity. CoRR Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
2022	—	`conf`	Improving Few-Shot Learning Through Multi-task Representation Learning Theory. ECCV (20) Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard
2021	J	`jnl`	Optimal Transport as a Defense Against Adversarial Attacks. CoRR Quentin Bouniot, Romaric Audigier, Angélique Loesch
2020	B	`conf`	Optimal Transport as a Defense Against Adversarial Attacks. ICPR Quentin Bouniot, Romaric Audigier, Angelique Loesch
2020	J	`jnl`	Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms. CoRR Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard
2020	—	`conf`	Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks. CVPR Workshops Quentin Bouniot, Romaric Audigier, Angélique Loesch

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.