My Publications

Full, and up-to-date publications: G. Scholar

2025

• FG 2025, “Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data”. [arXiv][Code]
• MIDL 2025, “PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization”. [arXiv][Code]
• “Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition”. [White paper]
• arXiv, “TeD-Loc: Text Distillation for Weakly Supervised Object Localization”. [arXiv][Code][Page]
• New challenge is now open @ 8TH ABAW @ CVPR 2025. “Ambivalence/Hesitancy (AH) Recognition Challenge at 8th Workshop and Competition on Affective & Behavior Analysis in-the-wild (ABAW)”. [Page][Results][White paper]
• Pattern Recognition journal, “CoLo-CAM: Class Activation Mapping for Object Co-Localization in Weakly-Labeled Unconstrained Videos”. [arXiv][Code][Page][Free journal access until March 14, 2025]
• WACV 2025, “A Realistic Protocol for Evaluation of Weakly Supervised Object Localization”. [arXiv][Code][Page]

2024

• AI and Digital Health Symposium, ETS Montreal, Canada. “Incorporating Affective Computing to Enhance Health Assessments and Interventions”. [Posters]
• NeurIPS 2024, “SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution”. [arXiv][Code][Download Dataset][Page][Hugging Face Spaces]
• ECCVw 2024, “Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild”. [arXiv][Code text-based][Code feature-based]
• Patent with Ericsson (Pending), “Method and System for Providing Labeled Images for Small Cell Site Selection”. [Link]
• CVPRw 2024, “Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology”. [arXiv][Code]
• CVPRw 2024, “Joint Multimodal Transformer for Dimensional Emotional Recognition in the Wild”. [arXiv][Code]
• FG 2024, “Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues”. [arXiv][Code]
• FG 2024, “Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport”. [arXiv][Code]
• FG 2024, “Subject-Based Domain Adaptation for Facial Expression Recognition”. [arXiv][Code]

2023

• Image and Vision Computing. “DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization”. [arXiv][Code]
• Summer School 2023-Jul.3-7-Concordia University-Montreal, Canada. “Leveraging Artificial Intelligence to Optimise Behavioural Health Interventions and Assessments”. [Website]
• MELBA journal, “Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey”. [Webpage][PDF][Code][MIDL2023 poster]
• WACVw 2023, “Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization”. [arXiv][Code]
• WACV 2023, “TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos”. [arXiv][Code]

2022-21-20

• Montreal AI Symposium, “Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization”. [arXiv][Code]
• ICPR tutorial, “Deep Learning Models for Weakly-Supervised Object Localization and Segmentation”. [Page][Slides]
• MIDL 2022, “Negative Evidence Matters in Interpretable Histology Image Classification”. [arXiv][Code]
• WACV 2022, “F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling”. [arXiv][Code]
• TMI journal, “Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty”. [arXiv][Code]

2019

• arXiv, “Deep Ordinal Classification with Inequality Constraints”. [arXiv][Code]
• arXiv, “Min-max Entropy for Weakly Supervised Pointwise Localization”. [arXiv][Code]

2018

• PhD thesis, “Neural Networks Regularization Through Representation Learning”. [arXiv][HAL-Thèse][Slides]
• Neurocomputing journal, “Deep Neural Networks Regularization for Structured Output Prediction”. [arXiv][Code]

2017

• arXiv, “Neural Networks Regularization Through Class-wise Invariant Representation Learning”. [arXiv][Code]
• Computers in Biology and Medicine journal, “Spotting L3 slice in CT scans using deep convolutional network and transfer learning”. [Page][Slides][Poster][Animation][Animation][Paris-Normandie][“BodyComp.AI” 2017 French Innovative Unicancer Prize]
• Japanese-French workshop on Optimization for machine learning, Riken and LITIS labs, “Neural Networks Regularization Through Representation Learning”. [Page][HAL-Thèse][Slides]
• UCA Deep Learning School, Nice, “Détection de la coupe L3 par CNN et transfert learning”. [Page][Slides]
• UCA Deep Learning School, Nice, “Representation Learning”. [Page][Slides]

2016

• European Symposium on Artificial Neural Networks (ESANN), “Deep multi-task learning with evolving weights”. [PDF][Slides]
• Reconnaissance des Formes et l’Intelligence Artificielle (RFIA) (Special session “Apprentissage et vision”), “Pondération dynamique dans un cadre multi-tâche pour réseaux de neurones profonds”. [PDF][Slides]
• Image and Signal Processing Seminars, Université Catholique de Louvain, “Deep Neural Networks and Structured Output Problems”. [Slides]
• GDR Information, Signal, Image et ViSion. Télécom ParisTech, “Détection de la Coupe L3 par CNN et Transfert Learning”. [Slides]
• Normastic, Journée « Deep Learning » de l’axe Données, Apprentissage, Connaissances. UFR des Science, Université de Caen Normandie, “Deep Multi-task Learning with Evolving Weights”. [Page][Slides]
• Journée Des Doctorants (JDD). Université le Havre Normandie, “Deep Multi-task Learning with Evolving Weights”. [Slides]

2015

• Conférence Francophone sur l’Apprentissage Automatique (CAP), “A Unified Neural Based Model For Structured Output Problems”. [PDF][Poster]
• International Conference on Machine Learning (ICML), Deep Learning Workshop, “Learning Structured Output Dependencies Using Deep Neural Networks””. [Slides]
• Normastic, l’axe Données, Apprentissage, Connaissances. UFR des Science, Université de Caen Normandie, “Deep Neural Architectures for Structured Output Problems”. [Slides]