7 papers
Affect-Prototype Guided Fusion for Open-Vocabulary Incomplete Multi-modal Emotion Recognition
Yichi Zhang, Shenyue Wang, Jing Luo +2
Open-vocabulary multimodal emotion recognition (OV-MER) aims to generate open natural-language emotion labels from multimodal affective cues. In real-world scenarios, however, comp…
FairMT: Fairness for Heterogeneous Multi-Task Learning
Guanyu Hu, Tangzheng Lian, Na Yan +5
Fairness in machine learning has been extensively studied in single-task settings, while fair multi-task learning (MTL), especially with heterogeneous tasks (classification, detect…
CausalAffect: Causal Discovery for Facial Affective Understanding
Guanyu Hu, Tangzheng Lian, Dimitrios Kollias +2
Understanding human affect from facial behavior requires not only accurate recognition but also structured reasoning over the latent dependencies that drive muscle activations and…
Grounding Emotion Recognition with Visual Prototypes: VEGA -- Revisiting CLIP in MERC
Guanyu Hu, Dimitrios Kollias, Xinyu Yang
Multimodal Emotion Recognition in Conversations remains a challenging task due to the complex interplay of textual, acoustic and visual signals. While recent models have improved p…
Fair Domain Generalization: An Information-Theoretic View
Tangzheng Lian, Guanyu Hu, Dimitrios Kollias +2
Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen t…
Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
Guanyu Hu, Dimitrios Kollias, Eleni Papadopoulou +3
Evaluating affect analysis methods presents challenges due to inconsistencies in database partitioning and evaluation protocols, leading to unfair and biased results. Previous stud…
Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensation
Guanyu Hu, Jie Wei, Siyang Song +4
The objective of the Multiple Appropriate Facial Reaction Generation (MAFRG) task is to produce contextually appropriate and diverse listener facial behavioural responses based on…