TL;DR
The proposed Mamba-CrossMod is a novel multimodal feature fusion framework that introduces Mamba-ATT, an enhanced attention mechanism based on a selective state-space model for capturing long-range dependencies with theoretically linear complexity.
Transformer-GAT is proposed, a hybrid framework that combines Transformer and the Graph Attention Network to enable cross-modal emotion understanding and effectively integrates multimodal features, balances global and local contexts, and provides deeper emotional insights, offering new directions for multimodal emotion...
A framework for learning adaptive cross-modal interactions for multimodal sentiment analysis that consistently outperforms previous methods and enhances multimodal representation capability for sentiment classification is proposed.
Experimental evaluations on two public datasets DEAP, AMIGOS and a private dataset MAN-II demonstrate that CLMER significantly outperforms unimodal and traditional fusion approaches, achieving state-of-the-art performance in emotion classification tasks.
Multimodal sentiment analysis aims to improve cross-modal fusion to understand sentiment better. Most existing methods rely on single-stage fusion or local cross-modal interactions, making it difficult to fully capture relationships among modalities, thereby limiting their sentiment representation capabilities and...
Multimodal emotion recognition is increasingly important for healthcare, education, and human-computer interaction. However, many existing systems learn a single shared representation for all emotions, which can blur subtle class-specific cues. This paper proposes an emotion-specific multimodal architecture that combin...
These findings reveal that multimodal systems, which fuse visual, acoustic, linguistic, linguistic, and physiological signals, consistently outperform unimodal counterparts, achieving accuracy levels above 85% on benchmark datasets.
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