用因果干预消除背景误导，提升真实情绪识别准确率
AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognition
- 引入注意力引导的因果干预模块，分离背景与情绪的虚假关联
- 在CAER-S数据集上达到当前最优性能，准确率显著提升
- 适合需要鲁棒情绪识别的智能交互系统开发者
上下文感知情绪识别（CAER）能提升现实场景中情感计算的实用性，但传统方法常受背景偏见影响——即背景信息与情绪标签之间存在虚假相关性（如将“花园”与“快乐”关联）。本文提出AGCD-Net，一种注意力引导的上下文去偏模型。其核心是融合空间变换网络与挤压-激励模块的混合卷积编码器（Hybrid ConvNeXt），增强特征重校准能力；并设计注意力引导的因果干预模块（AG-CIM），基于因果理论对上下文特征进行扰动，识别并消除虚假相关性，通过人脸特征引导修正。在CAER-S数据集上的实验表明，AGCD-Net实现当前最优性能，验证了因果去偏对复杂场景下鲁棒情绪识别的重要性。
原文摘要 · Abstract (English)
Context-aware emotion recognition (CAER) enhances affective computing in real-world scenarios, but traditional methods often suffer from context bias-spurious correlation between background context and emotion labels (e.g. associating ``garden'' with ``happy''). In this paper, we propose \textbf{AGCD-Net}, an Attention Guided Context Debiasing model that introduces \textit{Hybrid ConvNeXt}, a novel convolutional encoder that extends the ConvNeXt backbone by integrating Spatial Transformer Network and Squeeze-and-Excitation layers for enhanced feature recalibration. At the core of AGCD-Net is the Attention Guided - Causal Intervention Module (AG-CIM), which applies causal theory, perturbs context features, isolates spurious correlations, and performs an attention-driven correction guided by face features to mitigate context bias. Experimental results on the CAER-S dataset demonstrate the effectiveness of AGCD-Net, achieving state-of-the-art performance and highlighting the importance of causal debiasing for robust emotion recognition in complex settings.
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