  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.
