A method that derives a sampling distribution over modality-missing scenarios from latent-space distortions improves fine-tuning performance for multimodal semantic segmentation on remote sensing datasets compared to uniform dropout and LoRA adaptation.
Lora: Low-rank adaptation of large language models
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A token-based dual-view fusion framework inserts dedicated cross-attention fusion tokens at multiple depths of a frozen vision transformer to improve mammogram classification.
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Latent Space Guided Scenario Sampling for Multimodal Segmentation Under Missing Modalities
A method that derives a sampling distribution over modality-missing scenarios from latent-space distortions improves fine-tuning performance for multimodal semantic segmentation on remote sensing datasets compared to uniform dropout and LoRA adaptation.
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Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
A token-based dual-view fusion framework inserts dedicated cross-attention fusion tokens at multiple depths of a frozen vision transformer to improve mammogram classification.