362 research outputs found
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Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been recently proposed. However: i) their size is small, ii) they are not audiovisual, iii) only a small part is manually annotated, iv) they contain a small number of subjects, or v) they are not annotated for all main behavior tasks (valence-arousal estimation, action unit detection and basic expression classification). To address these, we substantially extend the largest available in-the-wild database (Aff-Wild) to study continuous emotions such as valence and arousal. Furthermore, we annotate parts of the database with basic expressions and action units. As a consequence, for the first time, this allows the joint study of all three types of behavior states. We call this database Aff-Wild2. We conduct extensive experiments with CNN and CNN-RNN architectures that use visual and audio modalities; these networks are trained on Aff-Wild2 and their performance is then evaluated on 10 publicly available emotion databases. We show that the networks achieve state-of-the-art performance for the emotion recognition tasks. Additionally, we adapt the ArcFace loss function in the emotion recognition context and use it for training two new networks on Aff-Wild2 and then re-train them in a variety of diverse expression recognition databases. The networks are shown to improve the existing state-of-the-art. The database, emotion recognition models and source code are available at http://ibug.doc.ic.ac.uk/resources/aff-wild2
Investigating Context Awareness of Affective Computing Systems: A Critical Approach
AbstractIntelligent Human Computer Interaction systems should be affective aware and Affective Computing systems should be context aware. Positioned in the cross-section of the research areas of Interaction Context and Affective Computing current paper investigates if and how context is incorporated in automatic analysis of human affective behavior. Several related aspects are discussed ranging from modeling, acquiring and annotating issues in affectively enhanced corpora to issues related to incorporating context information in a multimodal fusion framework of affective analysis. These aspects are critically discussed in terms of the challenges they comprise while, in a wider framework, future directions of this recently active, yet mainly unexplored, research area are identified. Overall, the paper aims to both document the present status as well as comment on the evolution of the upcoming topic of Context in Affective Computing
3D Convolutional and Recurrent Neural Networks for Reactor Perturbation Unfolding Anomaly Detection
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