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.
Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis.IEEE Transactions on Affective Computing, 14(3):2276–2289
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Missing-by-Design learns property-aware embeddings and uses saliency-driven Gaussian updates to produce machine-verifiable certificates that remove a chosen modality without full retraining.
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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Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis
Missing-by-Design learns property-aware embeddings and uses saliency-driven Gaussian updates to produce machine-verifiable certificates that remove a chosen modality without full retraining.
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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.