Hi!

My name is Soufiane Belharbi. I am a post-doc at LIVIA Lab., ÉTS, Montreal in collaboration with McCaffrey Lab. / GCRC McGill. I am working with Eric Granger, Ismail Ben Ayed, and Luke McCaffrey on training neural networks with weak supervision.

Research Interests

Machine learning
Neural networks
Representation learning
Weakly supervised learning
Interpretable machine learning

News

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]

Apr. 2025: MIDL 2025, “PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization”. [arXiv][Code]
Mar. 2025: New arXiv, “Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data”. [arXiv][Code]
Jan. 2025: New arXiv, “TeD-Loc: Text Distillation for Weakly Supervised Object Localization”. [arXiv][Code][Page]
Jan. 2025: 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]
Jan. 2025: 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]
Oct. 2024: WACV 2025, “A Realistic Protocol for Evaluation of Weakly Supervised Object Localization”. [arXiv][Code]
Oct. 2024: AI and Digital Health Symposium, ETS Montreal, Canada. “Incorporating Affective Computing to Enhance Health Assessments and Interventions”. [Posters]
Sept. 2024: NeurIPS 2024, “SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution”. [arXiv][Code][Download Dataset][Page]
Aug. 2024: ECCVw 2024, “Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild”. [arXiv][Code text-based][Code feature-based]
Jul. 2024: New patent with Ericsson (Pending), “Method and System for Providing Labeled Images for Small Cell Site Selection”. [Link]
Apr. 2024: CVPRw 2024, “Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology”. [arXiv][Code]
Apr. 2024: CVPRw 2024, “Joint Multimodal Transformer for Dimensional Emotional Recognition in the Wild”. [arXiv][Code]
Mar. 2024: FG 2024, “Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues”. [arXiv][Code]
Mar. 2024: FG 2024, “Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport”. [arXiv][Code]
Mar. 2024: FG 2024, “Subject-Based Domain Adaptation for Facial Expression Recognition”. [arXiv][Code]
Oct. 2023: Image and Vision Computing. “DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization”. [arXiv][Code]
Jul. 2023: Summer School 2023-Jul.3-7-Concordia University-Montreal, Canada. “Leveraging Artificial Intelligence to Optimise Behavioural Health Interventions and Assessments”. [Website]
Mar. 2023: MELBA journal, “Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey”. [Webpage][PDF][Code][MIDL2023 poster]
Nov. 2022: WACVw 2023, “Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization”. [arXiv][Code]
Oct. 2022: WACV 2023, “TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos”. [arXiv][Code]
Sep. 2022: Montreal AI Symposium, “Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization”. [arXiv][Code]
Aug. 2022: ICPR tutorial, “Deep Learning Models for Weakly-Supervised Object Localization and Segmentation”. [Page][Slides]
Feb. 2022: MIDL 2022, “Negative Evidence Matters in Interpretable Histology Image Classification”. [arXiv][Code]
Jan. 2022: WACV 2022, “F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling”. [arXiv][Code]
Jan. 2022: TMI journal, “Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty”. [arXiv][Code]

Phd

I completed my PhD in computer science at the Institut National des Sciences Appliquées de Rouen Normandie (INSA Rouen Normandie) in LITIS lab, Apprentissage (Learning) team (Oct. 2014-Jul.2018). I was supervised by Prof. Sébastien Adam (director), Clément Chatelain, and Romain Hérault. During my PhD, I conducted research on the regularization of neural networks through representation learning with particular focus on learning scenarios where only few training samples are available. [BibTeX] [ArXiv][HAL-Thèse][Presentation]

@phdthesis{sbelharbiphd2018,
  author = {Belharbi, S.},
  title = {Neural Networks Regularization Through Representation Learning},
  school = {Normandie Université, INSA Rouen Normandie, LITIS laboratory},
  year = {2018},
  pages = {196},
  note = {Supervisor: Sébastien Adam, Advisors: Clément Chatelain, Romain Hérault},
  url = {https://arxiv.org/pdf/1807.05292}
}