  Understanding emotions from diverse contexts has received widespread
attention in computer vision communities. The core philosophy of Context-Aware
Emotion Recognition (CAER) is to provide valuable semantic cues for recognizing
the emotions of target persons by leveraging rich contextual information.
Current approaches invariably focus on designing sophisticated structures to
extract perceptually critical representations from contexts. Nevertheless, a
long-neglected dilemma is that a severe context bias in existing datasets
results in an unbalanced distribution of emotional states among different
contexts, causing biased visual representation learning. From a causal
demystification perspective, the harmful bias is identified as a confounder
that misleads existing models to learn spurious correlations based on
likelihood estimation, limiting the models' performance. To address the issue,
we embrace causal inference to disentangle the models from the impact of such
bias, and formulate the causalities among variables in the CAER task via a
customized causal graph. Subsequently, we present a Contextual Causal
Intervention Module (CCIM) to de-confound the confounder, which is built upon
backdoor adjustment theory to facilitate seeking approximate causal effects
during model training. As a plug-and-play component, CCIM can easily integrate
with existing approaches and bring significant improvements. Systematic
experiments on three datasets demonstrate the effectiveness of our CCIM.
