TSDA

TSDA, short for Temporal-Spatial Decouple before Act, is a novel framework designed for Multimodal Sentiment Analysis (MSA). Traditional MSA methods often struggle with spatiotemporal heterogeneity, where temporal and spatial information within and across modalities are not uniformly distributed or aligned, leading to information asymmetry and suboptimal performance. TSDA tackles this by introducing a core mechanism that explicitly separates each input modality into distinct temporal dynamics and spatial structural contexts. This decoupling is achieved through dedicated temporal and spatial encoders, which project signals into separate feature spaces. The method then employs Factor-Consistent Cross-Modal Alignment to ensure that temporal features interact only with temporal counterparts and spatial features with spatial counterparts across modalities. This precise alignment, combined with factor-specific supervision and decorrelation regularization, minimizes cross-factor leakage while preserving complementarity. Finally, a Gated Recouple module integrates these aligned streams for the final task, enabling TSDA to outperform existing baselines in MSA by effectively managing complex spatiotemporal interactions. It is primarily used by researchers and engineers working on advanced multimodal learning systems, particularly in areas requiring nuanced understanding of human expressions.