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The coding and annotation of multimodal dialogue acts
, 2012
"... This paper describes how the ISO 24617-2 annotation scheme can be used, together with the DIT++ method of ‘multidimensional segmentation’, to annotate nonverbal and multimodal dialogue behaviour. We analyse the fundamental distinction between (a) the coding of surface features; (b) form-related sema ..."
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This paper describes how the ISO 24617-2 annotation scheme can be used, together with the DIT++ method of ‘multidimensional segmentation’, to annotate nonverbal and multimodal dialogue behaviour. We analyse the fundamental distinction between (a) the coding of surface features; (b) form-related semantic classification; and (c) semantic annotation in terms of dialogue acts, supported by experimental studies of (a) and (b). We discuss examples of specification languages for representing the results of each of these activities, showing how dialogue act annotations can be attached to XML representations of functional segments of multimodal data.
Facial Action Unit Detection by Cascade of Tasks
"... Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and neg-ative ones, where existing work emphasizes the use of dif-ferent featur ..."
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Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and neg-ative ones, where existing work emphasizes the use of dif-ferent features or classifiers. In this paper, we propose a method called Cascade of Tasks (CoT) that combines the use of different tasks (i.e., frame, segment and transition) for AU event detection. We train CoT in a sequential manner embracing diversity, which ensures robustness and general-ization to unseen data. In addition to conventional frame-based metrics that evaluate frames independently, we pro-pose a new event-based metric to evaluate detection perfor-mance at event-level. We show how the CoT method con-sistently outperforms state-of-the-art approaches in both frame-based and event-based metrics, across three public datasets that differ in complexity: CK+, FERA and RU-FACS. 1.
expressions is comparable to
"... Guess Who?: A game to crowdsource the labeling of affective facial ..."
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