DMIL is a multimodal learning framework that decomposes sample-specific interactions into redundant, unique, and synergistic components via variational architecture and uses them for adaptive fine-tuning.
What to align in multimodal con- trastive learning?
3 Pith papers cite this work. Polarity classification is still indexing.
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Contrastive Fusion (ConFu) adds a fused-modality contrastive term to jointly align individual modalities and their combinations, enabling capture of higher-order dependencies like XOR relations while preserving pairwise alignments.
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Information-Theoretic Decomposition for Multimodal Interaction Learning
DMIL is a multimodal learning framework that decomposes sample-specific interactions into redundant, unique, and synergistic components via variational architecture and uses them for adaptive fine-tuning.
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The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
Contrastive Fusion (ConFu) adds a fused-modality contrastive term to jointly align individual modalities and their combinations, enabling capture of higher-order dependencies like XOR relations while preserving pairwise alignments.