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Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
Since consistency and complementarity information correspond to low-frequency and high-frequency information, respectively, this paper revisits the problem of multimodal emotion recognition in conversation from the perspective of the graph spectrum.
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion
Specifically, on the one hand, in the feature disentanglement stage, we propose a Broad Mamba, which does not rely on a self-attention mechanism for sequence modeling, but uses state space models to compress emotional representation, and utilizes broad learning systems to explore the potential data distribution in broad space.
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).
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