EBMC framework enhances weaker modalities via semantic disentanglement and cross-modal boosting, then balances them with energy-guided coordination and instance-aware trust distillation for improved MSA performance and missing-modality robustness.
Learn- ing language-guided adaptive hyper-modality representation for mul- timodal sentiment analysis
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MMCI uses multi-relational graph modeling and attention-based disentanglement of causal versus shortcut features, combined with backdoor adjustment, to reduce bias and improve generalization in multimodal sentiment analysis.
A two-level reference alignment framework uses complete-modality samples and prototype voting to reduce decision drift and improve robustness in multimodal sentiment analysis under missing modalities.
Group Cognition Learning uses governed two-stage agents after separate modality encoding to mitigate dominance and spurious coupling, reporting state-of-the-art results on CMU-MOSI, CMU-MOSEI, and MIntRec for regression and classification.
Survey organizing multimodal affective computing research around four NLP tasks, method paradigms, datasets, evaluation protocols, and future directions while releasing a resource repository.
citing papers explorer
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Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis
EBMC framework enhances weaker modalities via semantic disentanglement and cross-modal boosting, then balances them with energy-guided coordination and instance-aware trust distillation for improved MSA performance and missing-modality robustness.
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Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis
MMCI uses multi-relational graph modeling and attention-based disentanglement of causal versus shortcut features, combined with backdoor adjustment, to reduce bias and improve generalization in multimodal sentiment analysis.
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Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities
A two-level reference alignment framework uses complete-modality samples and prototype voting to reduce decision drift and improve robustness in multimodal sentiment analysis under missing modalities.
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Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration
Group Cognition Learning uses governed two-stage agents after separate modality encoding to mitigate dominance and spurious coupling, reporting state-of-the-art results on CMU-MOSI, CMU-MOSEI, and MIntRec for regression and classification.
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Recent Advances in Multimodal Affective Computing: An NLP Perspective
Survey organizing multimodal affective computing research around four NLP tasks, method paradigms, datasets, evaluation protocols, and future directions while releasing a resource repository.