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The paper addresses Incomplete Multimodal Sentiment Analysis with Unseen Modality Combinations and proposes the ContrastiveMixedPromptLearning (CMPL) model, which uses label-guided contrastive learning and modality-combination prompts to enhance generalization, achieving over 5% accuracy improvement on benchmark datasets.
The paper proposes C²MOE, a Consistency and Complementarity-guided Mixture of Experts framework for incomplete multimodal emotion recognition in conversations, using information-theoretic decomposition to improve robustness when modalities are missing.