AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognition
This addresses robust emotion recognition in complex real-world scenarios for affective computing applications, representing a novel method for a known bottleneck.
The paper tackles context bias in emotion recognition, where background context spuriously correlates with emotion labels, by proposing AGCD-Net, which uses a novel attention-guided causal intervention module to isolate and correct these biases, achieving state-of-the-art performance on the CAER-S dataset.
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.