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.
Semantic segmentation with scale alignment and contextual information fusion for multimodal remote sensing images
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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.