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Publications | ![]() |
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Domaines Capteurs et modélisation 3D Réalité virtuelle et simulation Thermographie et vision infrarouge Traitement de sequences video Vision biologique Vision cognitive Projets E-Inclusion COBVIS-D Auto21 SKALPEL-ICT COGNOIS VERTEX VRES Chercheurs Christian Gagné Xavier Maldague Denis Poussart Simon Gagné Marc Parizeau Robert Bergevin André Zaccarin Jean-François Lalonde Abdel Hakim Bendada Dates Trier par date dans l'ordre decroissant |
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Sequential Emotion Recognition using Latent-Dynamic Conditional Neural FieldsAbstract - A wide number of problems in face and gesture analysis involve the labeling of temporal sequences. In this paper, we introduce a discriminative model for such sequence labeling tasks. This model involves two layers of latent dynamics, each with their separate roles. The first layer, the neural network or gating layer, aims to extract non-linear relationships between input data and output labels. The second layer, the hidden-states layer, aims to model temporal sub-structure in the sequence by learning hidden-states and their transition dynamics. A new regularization term is proposed for the training of this model, encouraging diversity between hidden-states. We evaluate the performance of this model on an audiovisual dataset of emotion recognition and compare it against other popular methods for sequence labeling. ![]() Bibtex:
@inproceedings{Lévesque950, Dernière modification: 2013/01/14 par jclev7 |
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