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• W2962918516 abstract "In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN). We jointly train a variational auto-encoder (VAE) and a generative adversarial network (GAN) to learn a powerful latent representation from footage of audiences viewing feature-length movies. We show that the learned latent representation successfully encodes meaningful signatures of behaviors related to audience engagement (smiling & laughing) and disengagement (yawning). Our results provide a proof of concept for a more general methodology for annotating hard-to-label multimedia data featuring sparse examples of signals of interest." @default.
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• W2962918516 date "2018-05-01" @default.
• W2962918516 modified "2023-09-23" @default.
• W2962918516 title "Unsupervised Deep Representations for Learning Audience Facial Behaviors" @default.
• W2962918516 hasPublicationYear "2018" @default.
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