Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.

翻译：上下文感知情感识别（CAER）近期推动了情感计算技术在无约束环境中的实际应用。主流CAER方法普遍通过融合多样化上下文信息与以主体为中心的特征来感知目标人物的情绪状态。尽管取得了进展，该领域仍面临由上下文偏置干扰带来的核心挑战。这种有害偏置迫使模型在似然估计中依赖背景上下文与情感标签间的伪相关性，导致严重的性能瓶颈并干扰有价值的上下文先验信息。本文提出反事实情感推理（CLEF）框架以解决上述问题。具体而言，我们首先构建广义因果图以解耦CAER中变量的因果关系。基于该因果图，CLEF引入非侵入式上下文分支来捕捉上下文偏置造成的有害直接效应。在推理过程中，通过对比事实结果与反事实结果，从总因果效应中消除直接上下文效应，从而实现偏置缓解与鲁棒预测。作为模型无关框架，CLEF可便捷集成至现有方法，并带来稳定的性能提升。