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pro vyhledávání: '"Bacon, Simon"'
Autor:
Belharbi, Soufiane, Pedersoli, Marco, Koerich, Alessandro Lameiras, Bacon, Simon, Granger, Eric
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. Experts typically associate spatial action units (AUs) from a codebook
Externí odkaz:
http://arxiv.org/abs/2410.01848
Autor:
Richet, Nicolas, Belharbi, Soufiane, Aslam, Haseeb, Schadt, Meike Emilie, González-González, Manuela, Cortal, Gustave, Koerich, Alessandro Lameiras, Pedersoli, Marco, Finkel, Alain, Bacon, Simon, Granger, Eric
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 individual basic emotions. However, compound emotions often occur in re
Externí odkaz:
http://arxiv.org/abs/2407.12927
Autor:
Waligora, Paul, Aslam, Haseeb, Zeeshan, Osama, Belharbi, Soufiane, Koerich, Alessandro Lameiras, Pedersoli, Marco, Bacon, Simon, Granger, Eric
Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that
Externí odkaz:
http://arxiv.org/abs/2403.10488
Autor:
Belharbi, Soufiane, Pedersoli, Marco, Koerich, Alessandro Lameiras, Bacon, Simon, Granger, Eric
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. Experts typically associate spatial action units (\aus) from a codeboo
Externí odkaz:
http://arxiv.org/abs/2402.00281
Autor:
Aslam, Muhammad Haseeb, Zeeshan, Muhammad Osama, Belharbi, Soufiane, Pedersoli, Marco, Koerich, Alessandro, Bacon, Simon, Granger, Eric
Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because of their ability to learn complementary and redundant semantic information. However, these models struggle in
Externí odkaz:
http://arxiv.org/abs/2401.15489
Autor:
Zeeshan, Muhammad Osama, Aslam, Muhammad Haseeb, Belharbi, Soufiane, Koerich, Alessandro Lameiras, Pedersoli, Marco, Bacon, Simon, Granger, Eric
Adapting a deep learning model to a specific target individual is a challenging facial expression recognition (FER) task that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt
Externí odkaz:
http://arxiv.org/abs/2312.05632