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.
Improving multimodal fusion with hierarchical mutual in- formation maximization for multimodal sentiment analy- sis.arXiv preprint arXiv:2109.00412
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READ recurrent adapters with partial video-language alignment via optimal transport outperform standard fine-tuning on low-resource temporal grounding and summarization tasks.
CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.
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.
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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READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling
READ recurrent adapters with partial video-language alignment via optimal transport outperform standard fine-tuning on low-resource temporal grounding and summarization tasks.
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Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition
CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.
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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.