Mood as a Contextual Cue for Improved Emotion Inference

Abstract

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.