20 citations · 22 across the 25 of their papers we have counts for
27 papers
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…
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…
Brazilian Portuguese Image Captioning with Transformers: A Study on Cross-Native-Translated Dataset
Gabriel Bromonschenkel, Alessandro L. Koerich, Thiago M. Paixão +1
Image captioning (IC) refers to the automatic generation of natural language descriptions for images, with applications ranging from social media content generation to assisting in…
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…
Automatic Proficiency Assessment in L2 English Learners
Armita Mohammadi, Alessandro Lameiras Koerich, Laureano Moro-Velazquez +1
Second language proficiency (L2) in English is usually perceptually evaluated by English teachers or expert evaluators, with the inherent intra- and inter-rater variability. This p…
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…
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…
Music Genre Classification using Large Language Models
Mohamed El Amine Meguenani, Alceu de Souza Britto, Alessandro Lameiras Koerich
This paper exploits the zero-shot capabilities of pre-trained large language models (LLMs) for music genre classification. The proposed approach splits audio signals into 20 ms chu…
AI-Driven Early Mental Health Screening: Analyzing Selfies of Pregnant Women
Gustavo A. Basílio, Thiago B. Pereira, Alessandro L. Koerich +9
Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective…
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.…
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…
Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild
Nicolas Richet, Soufiane Belharbi, Haseeb Aslam +8
Systems for multimodal emotion recognition (ER) are commonly trained to extract features from different modalities (e.g., visual, audio, and textual) that are combined to predict i…
Joint Multimodal Transformer for Emotion Recognition in the Wild
Paul Waligora, Haseeb Aslam, Osama Zeeshan +5
Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e.g., visual, textual, physiolog…
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
Israel A. Laurensi, Alceu de Souza Britto, Jean Paul Barddal +1
Facial expression recognition is a pivotal component in machine learning, facilitating various applications. However, convolutional neural networks (CNNs) are often plagued by cata…
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities
Luciana Trinkaus Menon, Luiz Carlos Ribeiro Neduziak, Jean Paul Barddal +2
The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI). Mul…
Methods for Generating Drift in Text Streams
Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1
Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on.…
Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1
The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products. Given th…
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
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.…