Multimodal Emotion Recognition via Fusion of Mamba and Liquid Neural Networks with Cross-Modal Alignment

This paper proposes a novel multimodal emotion recognition framework, termed Sparse Alignment and Liquid-Mamba (SALM), which effectively integrates the strengths of Mamba networks and Liquid Neural Networks (LNNs).The proposed SALM model leverages sparse alignment for efficient cross-modal mapping and employs the Liquid-Mamba architecture to construct a robust and generalizable classifier. The dataset consists of multimodal signal-derived heatmaps and signal-aligned features obtained through Optimal Transport (OT) methods. It also includes partial code for the classifier.