情感碰撞：双双曲镜面流形用于通过反情感反射进行情感恢复
Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
AI总结：
本文提出Emotion Collider网络，利用双双曲镜面流形实现多模态情感和情绪建模，通过双曲空间对比学习提升类别分离，并在标准多模态情感基准上验证了其鲁棒性和准确性。
摘要：
情感表达是自然交流和有效人机交互的基础。我们提出了Emotion Collider (EC-Net)，一种双曲超图框架，用于多模态情感和情绪建模。EC-Net使用庞加莱球嵌入表示模态层次，并通过超图机制在节点和超边之间双向传递信息。为了增强类别分离，对比学习在双曲空间中以解耦的径向和角目标形式进行公式化。通过自适应超边构造保留时间步和模态间的高阶语义关系。在标准多模态情感基准上的实验证明，EC-Net生成稳健且语义连贯的表示，并在模态部分可用或受噪声污染时一致提高准确性。这些发现表明，显式层次几何与超图融合相结合，对于鲁棒的多模态情感理解是有效的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
