SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data

AI Digest

SemMSA通过LLM构建语义桥梁，解决多模态情感分析中不完整数据的伪生成问题，实现跨模态谱对齐与状态优化。

SemMSA addresses multimodal sentiment analysis with incomplete data by leveraging LLMs for semantic alignment and spectral alignment across modalities.

Key points

• 提出CSR模块通过适配器提取多模态表征并生成连续语义状态 CSR module extracts multimodal representations via adapters and generates continuous semantic states
• CSA模块通过增强核Gram矩阵谱成分实现跨模态对齐 CSA module aligns modalities by enhancing kernel Gram matrix spectral components
• 引入实例级谱分离约束防止表示坍缩 Instance-level spectral separation constraint prevents representation collapse
• 在SIMS/MOSI/MOSEI基准上达成SOTA性能 Achieves SOTA performance on SIMS/MOSI/MOSEI benchmarks

Takeaway: SemMSA通过语义引导的谱对齐突破多模态情感分析的不完整数据瓶颈。 / SemMSA breaks through incomplete data challenges in multimodal sentiment analysis through semantic-guided spectral alignment.

Why it matters 该方法为处理真实场景中缺失模态数据提供了鲁棒性解决方案，具有实际应用价值。