20 citations · 22 across the 25 of their papers we have counts for
12 papers · 1 filter
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.…
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
Muhammad Haseeb Aslam, Muhammad Osama Zeeshan, Soufiane Belharbi +4
Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because of their ability to learn complementary…