用双曲几何和超图融合提升多模态情感识别鲁棒性
Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
- 采用双曲空间嵌入表示模态层级关系
- 在噪声或缺失模态下仍保持高准确率
- 适合需要抗干扰能力的情感计算场景
情感表达是自然交流与人机交互的核心。本文提出情感碰撞器（EC-Net），一种基于双曲超图的多模态情感与情绪建模框架。该方法使用庞加莱球嵌入表示模态层次结构，并通过双向消息传递机制在节点与超边间融合信息。为增强类别区分，对比学习在双曲空间中以解耦的径向与角度目标进行。通过自适应构建超边，保留跨时间步与模态的高阶语义关联。在标准多模态情感基准上的实验表明，EC-Net生成了鲁棒且语义一致的表示，在部分模态缺失或受噪声污染时仍显著提升准确率。结果表明，显式层次几何结合超图融合可有效提升多模态情感理解的韧性。
原文摘要 · Abstract (English)
Emotional expression underpins natural communication and effective human-computer interaction. We present Emotion Collider (EC-Net), a hyperbolic hypergraph framework for multimodal emotion and sentiment modeling. EC-Net represents modality hierarchies using Poincare-ball embeddings and performs fusion through a hypergraph mechanism that passes messages bidirectionally between nodes and hyperedges. To sharpen class separation, contrastive learning is formulated in hyperbolic space with decoupled radial and angular objectives. High-order semantic relations across time steps and modalities are preserved via adaptive hyperedge construction. Empirical results on standard multimodal emotion benchmarks show that EC-Net produces robust, semantically coherent representations and consistently improves accuracy, particularly when modalities are partially available or contaminated by noise. These findings indicate that explicit hierarchical geometry combined with hypergraph fusion is effective for resilient multimodal affect understanding.
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