S3 decomposes multimodal data into selectable semantic experts, routes them adaptively, and sparsifies to achieve higher accuracy on MultiBench benchmarks with peak performance at intermediate sparsity levels.
Ur-funny: A mul- timodal language dataset for understanding humor
6 Pith papers cite this work. Polarity classification is still indexing.
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CDPR uses an intuition pathway for cross-modal consensus and a reasoning pathway for quantifying and mitigating inconsistencies to improve multimodal intent recognition.
TwistedHumor dataset shows dark humor in YouTube Shorts clusters around critique, coping, awkwardness and identity with more mixed and toxic audience reactions than regular humor.
CUCI-Net abstracts context-utterance dependency into an interpretation cue that combines local modality signals with global context and feeds it into the final multimodal interaction for context-conditioned predictions.
Introduces self-captioning and a Multimodal Interaction Gate to amplify redundant multimodal interactions, reporting 38.3% reduction in visual-induced errors and 16.8% consistency improvement.
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.
citing papers explorer
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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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Beyond Isolated Utterances: Cue-Guided Interaction for Context-Dependent Conversational Multimodal Understanding
CUCI-Net abstracts context-utterance dependency into an interpretation cue that combines local modality signals with global context and feeds it into the final multimodal interaction for context-conditioned predictions.