An unsupervised multilingual laughter segmentation method using Isolation Forest on BYOL-A audio representations outperforms existing supervised methods on non-English datasets.
UR- FUNNY: A Multimodal Language Dataset for Understanding Humor
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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.
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.
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MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method
An unsupervised multilingual laughter segmentation method using Isolation Forest on BYOL-A audio representations outperforms existing supervised methods on non-English datasets.
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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.
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Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.