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With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with emotional intelligence. Affective Computing involves the design, evalua
Externí odkaz:
http://arxiv.org/abs/2409.07256
Psychological studies observe that emotions are rarely expressed in isolation and are typically influenced by the surrounding context. While recent studies effectively harness uni- and multimodal cues for emotion inference, hardly any study has consi
Externí odkaz:
http://arxiv.org/abs/2402.08413
Autor:
Parameshwara, Ravikiran, Radwan, Ibrahim, Asthana, Akshay, Abbasnejad, Iman, Subramanian, Ramanathan, Goecke, Roland
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases. We propose Multi-Task Contrastiv
Externí odkaz:
http://arxiv.org/abs/2308.02173
Autor:
Gahalawat, Monika, Rojas, Raul Fernandez, Guha, Tanaya, Subramanian, Ramanathan, Goecke, Roland
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed \emph{kinemes}, for depre
Externí odkaz:
http://arxiv.org/abs/2307.12241
Autor:
Narayana, Soujanya, Radwan, Ibrahim, Parameshwara, Ravikiran, Abbasnejad, Iman, Asthana, Akshay, Subramanian, Ramanathan, Goecke, Roland
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention. Building on prior work, we (a) deduce and incorporate emot
Externí odkaz:
http://arxiv.org/abs/2306.06979
While emotion and mood interchangeably used, they differ in terms of duration, intensity and attributes. Even as multiple psychology studies examine the mood-emotion relationship, mood prediction has barely been studied. Recent machine learning advan
Externí odkaz:
http://arxiv.org/abs/2303.06632
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to e
Externí odkaz:
http://arxiv.org/abs/2302.09817
Although the terms mood and emotion are closely related and often used interchangeably, they are distinguished based on their duration, intensity and attribution. To date, hardly any computational models have (a) examined mood recognition, and (b) mo
Externí odkaz:
http://arxiv.org/abs/2210.00719
We present \textbf{ACAD}, an \textbf{a}ffective \textbf{c}omputational \textbf{ad}vertising framework expressly derived from perceptual metrics. Different from advertising methods which either ignore the emotional nature of (most) programs and ads, o
Externí odkaz:
http://arxiv.org/abs/2207.07297
Autor:
Parameshwara, Ravikiran, Narayana, Soujanya, Murugappan, Murugappan, Subramanian, Ramanathan, Radwan, Ibrahim, Goecke, Roland
While Parkinson's disease (PD) is typically characterized by motor disorder, there is evidence of diminished emotion perception in PD patients. This study examines the utility of affective Electroencephalography (EEG) signals to understand emotional
Externí odkaz:
http://arxiv.org/abs/2202.12936