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- W2979487532 abstract "Emotions play an important role in people's life. Understanding and recognising is not only important for interpersonal communication, but also has promising applications in Human-Computer Interaction, automobile safety and medical research. This project focuses on extending the emotion recognition database, and training the CNN + RNN emotion recognition neural networks with emotion category representation and valence & arousal representation. The combined models are constructed by training the two representations simultaneously. The comparison and analysis between the three types of model are discussed. The inner-relationship between two emotion representations and the interpretability of the neural networks are investigated. The findings suggest that categorical emotion recognition performance can benefit from training with a combined model. And the mapping of emotion category and valence & arousal values can explain this phenomenon." @default.
- W2979487532 created "2019-10-18" @default.
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- W2979487532 date "2019-10-13" @default.
- W2979487532 modified "2023-09-27" @default.
- W2979487532 title "Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild" @default.
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- W2979487532 doi "https://doi.org/10.48550/arxiv.1910.05784" @default.
- W2979487532 hasPublicationYear "2019" @default.
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