7 citations · 13 across the 34 of their papers we have counts for
38 papers · 1 filter
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…
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…
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…
TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Shakeeb Murtaza, Soufiane Belharbi, Alexis Guichemerre +2
Weakly supervised object localization (WSOL) models can predict both the object class and the spatial regions corresponding to the object, without requiring explicit bounding-box a…
Visual Modality Prompt for Adapting Vision-Language Object Detectors
Heitor R. Medeiros, Atif Belal, Srikanth Muralidharan +2
The zero-shot performance of object detectors degrades when tested on different modalities, such as infrared and depth. While recent work has explored image translation techniques…
Spatial Action Unit Cues for Interpretable Deep Facial Expression Recognition
Soufiane Belharbi, Marco Pedersoli, Alessandro Lameiras Koerich +2
Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretability, an important feature for end-users.…
Source-Free Domain Adaptation for YOLO Object Detection
Simon Varailhon, Masih Aminbeidokhti, Marco Pedersoli +1
Source-free domain adaptation (SFDA) is a challenging problem in object detection, where a pre-trained source model is adapted to a new target domain without using any source domai…
Multi Teacher Privileged Knowledge Distillation for Multimodal Expression Recognition
Muhammad Haseeb Aslam, Marco Pedersoli, Alessandro Lameiras Koerich +1
Human emotion is a complex phenomenon conveyed and perceived through facial expressions, vocal tones, body language, and physiological signals. Multimodal emotion recognition syste…
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
Shakeeb Murtaza, Marco Pedersoli, Aydin Sarraf +1
Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to as tags. State-of-the-art WSVOL methods…