An Occam’s Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets

Valentin Vielzeuf 1, 2 Corentin Kervadec 1 Stéphane Pateux 1 Alexis Lechervy 2 Frédéric Jurie 2
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest earning methods: i) transfer learning and low-dimensional space embedding allows to reduce the dimensionality of the representations. ii) The isual temporal information is handled by a simple score-per-frame selection process, averaged across time. iii) A simple frame selection echanism is also proposed to weight the images of a sequence. iv) The fusion of the different modalities is performed at prediction level (late usion). We also highlight the inherent challenges of the AFEW dataset and the difficulty of model selection with as few as 383 validation equences. The proposed real-time emotion classifier achieved a state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at he Emotion in the Wild 2018 challenge.
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https://hal.archives-ouvertes.fr/hal-01854019
Contributeur : Corentin Kervadec <>
Soumis le : lundi 6 août 2018 - 11:03:16
Dernière modification le : jeudi 9 août 2018 - 01:14:15
Document(s) archivé(s) le : mercredi 7 novembre 2018 - 13:34:49

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  • HAL Id : hal-01854019, version 1
  • ARXIV : 1808.02668

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Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Alexis Lechervy, Frédéric Jurie. An Occam’s Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets. ICMI (EmotiW) 2018, Oct 2018, Boulder, Colorado, United States. 〈hal-01854019〉

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