URMF uses learnable Gaussian posteriors to estimate modality-specific uncertainty and adjust fusion weights for improved multimodal sarcasm detection on MSD and MMSD2 benchmarks.
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Conditional flow matching produces segmentation samples whose pixel-wise variance quantifies aleatoric uncertainty in medical images by learning an exact density rather than relying on stochastic diffusion sampling.
T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.
citing papers explorer
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URMF: Uncertainty-aware Robust Multimodal Fusion for Multimodal Sarcasm Detection
URMF uses learnable Gaussian posteriors to estimate modality-specific uncertainty and adjust fusion weights for improved multimodal sarcasm detection on MSD and MMSD2 benchmarks.
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Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching
Conditional flow matching produces segmentation samples whose pixel-wise variance quantifies aleatoric uncertainty in medical images by learning an exact density rather than relying on stochastic diffusion sampling.
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T-DuMpRa: Teacher-guided Dual-path Multi-prototype Retrieval Augmented framework for fine-grained medical image classification
T-DuMpRa fuses classifier outputs with cosine-matched multi-prototypes from a teacher model via conservative gating, yielding 0.21-2.69% gains on skin lesion datasets across five backbones.