Abstract
Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world data often suffer from missing modalities due to transmission errors or user behavior, severely degrading model performance. Existing methods enhance robustness via cross-modal consistency learning but largely ignore modality complementarity, leading to biased reconstructions. To address this limitation, we propose CMOE, a novel Consistency and Complementarity-guided Mixture of Experts framework for incomplete multimodal emotion learning. Our approach unifies representation learning and missing modality imputation within a principled information-theoretic framework. Specifically, multimodal knowledge is factorized into consistency and complementarity components via interaction-aware experts. Consistency is captured by maximizing cross-modal predictability, while complementarity is preserved by maximizing conditional entropy between modalities. Building upon this decomposition, CMOE introduces a dual-branch prediction mechanism for robust imputation under missing modalities. The consistency branch aligns imputed features with the joint distribution by minimizing uncertainty, and the complementarity branch exploits modality-unique cues via entropy maximization. Finally, CMOE employs a learnable reweighting module that dynamically assigns importance scores to each expert's output, yielding a robust and adaptive fusion for imputation. Extensive experiments on multiple MERC benchmarks demonstrate that CMOE consistently surpasses state-of-the-art methods across various missing-modality settings, validating its robustness and generalization.