Éric Granger

BEACON: Behavior and Appearance Control for Subject-Specific Video Generation

Pokrzywa Baptiste, Nabyl Quignon, Yara Bahram +3

Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In addition to reproducing appearance, a…

DESA-TTA: Dynamic EMA and Source Anchoring for Test-Time Adaptation

Atif Belal, Lilian Hollard, Marco Pedersoli +1

Vision-language object detectors (VLODs) achieve strong zero-shot performance but remain vulnerable to distribution shifts during deployment. Mean-teacher methods for test-time ada…

TTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs

Muhammad Haseeb Aslam, Alessandro Koerich, Marco Pedersoli +2

Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the wild. ER is inherently multimodal, requiri…

Test-Time Adaptation with Online Personalized Energy-Based Cache for Fine-Grained Video Expression Recognition

Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi +3

Facial expression recognition (FER) in videos is challenging because models must identify subtle, temporally evolving affective states that vary across individuals. Although vision…

From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition

Dimitrios Kollias, Panagiotis Tzirakis, Alan Cowen +10

The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analysis, understanding of human…

Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan +8

Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcome…

CLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition

Muhammad Osama Zeeshan, Masoumeh Sharafi, Benoit Savary +3

Personalization in emotion recognition (ER) is essential for accurate interpretation of subtle and subject-specific expressive patterns. Recent advances in vision-language models (…

Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos

Masoumeh Sharafi, Muhammad Osama Zeeshan, Soufiane Belharbi +3

Facial expression recognition (FER) in videos requires model personalization to capture considerable variation across subjects. Vision-language models (VLMs) offer strong transfer…

LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups

Masih Aminbeidokhti, Subhankar Roy, Eric Granger +2

Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows…

High-Rate Mixout: Revisiting Mixout for Robust Domain Generalization

Masih Aminbeidokhti, Heitor Rapela Medeiros, Srikanth Muralidharan +2

Ensembling fine-tuned models initialized from powerful pre-trained weights is a common strategy to improve robustness under distribution shifts, but it comes with substantial compu…

Robust and Efficient Finetuning of Vision Foundation Models via Implicit Ensembling

Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger +1

Robust finetuning of foundation models seeks to improve out-of-distribution (OOD) generalization during in-distribution (ID) adaptation. We revisit Mixout, a stochastic regularizer…

VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors

Atif Belal, Heitor R. Medeiros, Marco Pedersoli +1

Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, but their performance degrades under distribution shift. Tes…

MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training

Muhammad Osama Zeeshan, Natacha Gillet, Alessandro Lameiras Koerich +3

Personalized expression recognition (ER) involves adapting a machine learning model to subject-specific data for improved recognition of expressions with considerable interpersonal…

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method

Masoumeh Sharafi, Soufiane Belharbi, Muhammad Osama Zeeshan +6

Facial expression recognition (FER) models are widely used in video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, d…

Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification

Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal +2

Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) meth…

Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?

Srikanth Muralidharan, Heitor R. Medeiros, Masih Aminbeidokhti +2

Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same time run on small embedded devices, with…

WiSE-OD: Benchmarking Robustness in Infrared Object Detection

Heitor R. Medeiros, Atif Belal, Masih Aminbeidokhti +2

Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weigh…

BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change

Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan +6

Ambivalence and hesitancy (A/H), closely related constructs, are the primary reasons why individuals delay, avoid, or abandon health behaviour changes. They are subtle and conflict…

Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation

Muhammad Haseeb Aslam, Clara Martinez, Marco Pedersoli +3

Advances in self-distillation have shown that when knowledge is distilled from a teacher to a student using the same deep learning (DL) architecture, the student performance can su…

Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation

Nairouz Mrabah, Nicolas Richet, Ismail Ben Ayed +1

Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the…

Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition

Muhammad Osama Zeeshan, Marco Pedersoli, Alessandro Lameiras Koerich +1

Personalized facial expression recognition (FER) involves adapting a machine learning model using samples from labeled sources and unlabeled target domains. Given the challenges of…

Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data

Masoumeh Sharafi, Emma Ollivier, Muhammad Osama Zeeshan +5

Facial Expression Recognition (FER) from videos is a crucial task in various application areas, such as human-computer interaction and health diagnosis and monitoring (e.g., assess…