A dual-branch graph model with modality disentanglement and speaker-aware hypergraphs outperforms baselines on IEMOCAP and MELD for multimodal conversational emotion recognition.
MMGCN: Multimodal fusion via deep graph convolution network for emotion recognition in conversation
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Hyper-MML integrates EEG, audio, and video using an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) and Adaptive Hypergraph Fusion Module (AHFM) to outperform prior methods on EAV and AFFEC datasets for conversational emotion recognition.
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.
citing papers explorer
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Disentangled Dual-Branch Graph Learning for Conversational Emotion Recognition
A dual-branch graph model with modality disentanglement and speaker-aware hypergraphs outperforms baselines on IEMOCAP and MELD for multimodal conversational emotion recognition.
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Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation
Hyper-MML integrates EEG, audio, and video using an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) and Adaptive Hypergraph Fusion Module (AHFM) to outperform prior methods on EAV and AFFEC datasets for conversational emotion recognition.
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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.