EC-Net combines Poincare-ball hyperbolic embeddings, hypergraph fusion, and decoupled radial-angular contrastive learning to improve accuracy on multimodal emotion benchmarks especially under partial or noisy modalities.
Multimodal sentiment analysis: a survey of methods, trends, and challenges.ACM Computing Surveys, 55(13s):1–38
2 Pith papers cite this work. Polarity classification is still indexing.
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ModalImmune enforces modality immunity in multimodal models by controlled collapse of input channels during training using adaptive regularizers and meta-optimization.
citing papers explorer
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Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
EC-Net combines Poincare-ball hyperbolic embeddings, hypergraph fusion, and decoupled radial-angular contrastive learning to improve accuracy on multimodal emotion benchmarks especially under partial or noisy modalities.
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ModalImmune: Immunity Driven Unlearning via Self Destructive Training
ModalImmune enforces modality immunity in multimodal models by controlled collapse of input channels during training using adaptive regularizers and meta-optimization.