Mood as a Contextual Cue for Improved Emotion Inference
<|reference_start|>Mood as a Contextual Cue for Improved Emotion Inference: 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 considered the effect of long-term affect, or \emph{mood}, on short-term \emph{emotion} inference. This study (a) proposes time-continuous \emph{valence} prediction from videos, fusing multimodal cues including \emph{mood} and \emph{emotion-change} ($\Delta$) labels, (b) serially integrates spatial and channel attention for improved inference, and (c) demonstrates algorithmic generalisability with experiments on the \emph{EMMA} and \emph{AffWild2} datasets. Empirical results affirm that utilising mood labels is highly beneficial for dynamic valence prediction. Comparing \emph{unimodal} (training only with mood labels) vs \emph{multimodal} (training with mood and $\Delta$ labels) results, inference performance improves for the latter, conveying that both long and short-term contextual cues are critical for time-continuous emotion inference.<|reference_end|>
@article{narayana2024mood,
    title={Mood as a Contextual Cue for Improved Emotion Inference},
    author={Soujanya Narayana, Ibrahim Radwan, Ramanathan Subramanian, Roland
  Goecke},
    journal={arXiv preprint arXiv:2402.08413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08413},
    primaryClass={cs.HC}
}