aiqbench analysis
This paper introduces EC-Net, a novel hyperbolic hypergraph framework designed to enhance multimodal sentiment analysis by leveraging Poincaré-ball embeddings and adaptive fusion mechanisms. The research demonstrates that this geometric approach yields robust representations capable of maintaining accuracy even when input modalities are incomplete or noisy. By explicitly modeling hierarchical relationships in non-Euclidean space, the method advances the state-of-the-art for resilient affect understanding systems.
Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
arXiv:2602.16161v4 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Multimedia (cs.MM); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2602.16161 [cs.MM]
(or arXiv:2602.16161v4 [cs.MM] for this version)
Full text under the publisher's open licence · 141 words
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