Publications

MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training
Muhammad Osama Zeeshan
Natacha Gillet
Alessandro Lameiras Koerich
Marco Pedersoli
Francois Bremond
Eric Granger
2025-08-01
arXiv (publié)
doi.org
arxiv.org
Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi
Soufiane Belharbi
Houssem Ben Salem
Ali Etemad
Alessandro Lameiras Koerich
Marco Pedersoli
Simon Bacon
Eric Granger
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interac… (voir plus)tion and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain data, thereby avoiding data privacy, storage, and transmission constraints. This paper addresses a challenging scenario where source data is unavailable for adaptation, and only unlabeled target data consisting solely of neutral expressions is available. SFDA methods are not typically designed to adapt using target data from only a single class. Further, using models to generate facial images with non-neutral expressions can be unstable and computationally intensive. In this paper, personalized feature translation (PFT) is proposed for SFDA. Unlike current image translation methods for SFDA, our lightweight method operates in the latent space. We first pre-train the translator on the source domain data to transform the subject-specific style features from one source subject into another. Expression information is preserved by optimizing a combination of expression consistency and style-aware objectives. Then, the translator is adapted on neutral target data, without using source data or image synthesis. By translating in the latent space, PFT avoids the complexity and noise of face expression generation, producing discriminative embeddings optimized for classification. Using PFT eliminates the need for image synthesis, reduces computational overhead (using a lightweight translator), and only adapts part of the model, making the method efficient compared to image-based translation.
2025-08-01
arXiv (publié)
doi.org
arxiv.org
Posttraumatic Growth in Intensive Care Unit Health Care Professionals After COVID-19
Elie Azoulay
Laurent Argaud
Vincent Labbé
Guillaume Dumas
Fabrice Bruneel
Mercé Jourdain
Christophe Guitton
Amélie Seguin
Samir Jaber
David Schnell
Isabelle Vinatier
Fanny Ardisson
Michel Ramakers
Antoine Herault
Olivier Lesieur
Alain Cariou
Antoine Vieillard-Baron
Olivier Guisset
Frédéric Pochard
Michael Darmon … (voir 1 de plus)
Nancy Kentish-Barnes
2025-08-01
JAMA Network Open (publié)
doi.org
The Promise of RL for Autoregressive Image Editing
Saba Ahmadi
Rabiul Awal
Ankur Sikarwar
Amirhossein Kazemnejad
Ge Ya Luo
Juan A. Rodriguez
Sai Rajeswar
Siva Reddy
Chris Pal
Benno Krojer
Aishwarya Agrawal
We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (voir plus)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.
2025-08-01
arXiv (publié)
doi.org
arxiv.org
The Promise of RL for Autoregressive Image Editing
Saba Ahmadi
Rabiul Awal
Ankur Sikarwar
Amirhossein Kazemnejad
Ge Ya Luo
Juan A. Rodriguez
Sai Rajeswar
Siva Reddy
Chris Pal
Benno Krojer
Aishwarya Agrawal
We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learn… (voir plus)ing (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.
2025-08-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Towards an Interpretable Machine Learning Model for Predicting Antimicrobial Resistance
Mohamed Mediouni
Vladimir Makarenkov
Abdoulaye Banire Diallo
2025-08-01
Journal of Global Antimicrobial Resistance (publié)
doi.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (voir plus)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
arXiv (publié)
doi.org
arxiv.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (voir plus)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Zero-Shot Anomaly Detection with Dual-Branch Prompt Learning
Zihan Wang
Samira Ebrahimi Kahou
Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable featur… (voir plus)es rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.
2025-08-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
2025-07-31
Science (publié)
doi.org
arxiv.org
Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott R. Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
Policy must be informed by, but also facilitate the generation of, scientific evidence.
2025-07-31
ArXiv (prépublication)
doi.org
arxiv.org
Computing Approximate Nash Equilibria for Integer Programming Games
Aloïs Duguet
Margarida Carvalho
Gabriele Dragotto
Sandra-ulrich Ngueveu
2025-07-31
Optimization Letters (publié)
doi.org
arxiv.org