Latent Space Guided Sampling for Multimodal Segmentation Under Missing Modalities
A new training strategy for multimodal semantic segmentation under missing modalities has been proposed. The method, detailed in a paper on arXiv (2605.20372), addresses the challenge of incomplete sensor data in remote sensing due to failures or adverse conditions. Instead of uniform random modality dropout, it learns a scenario sampling distribution from the pretrained latent space, guiding fine-tuning toward more informative modality availability scenarios. The approach quantifies each scenario's effect based on induced distortion, aiming to improve performance when modalities are missing.
Key facts
- arXiv paper 2605.20372 proposes a novel training strategy for multimodal segmentation under missing modalities.
- The method learns a scenario sampling distribution from the pretrained latent space.
- It replaces uniform random modality dropout with guided fine-tuning toward informative scenarios.
- The approach quantifies each scenario's effect based on induced distortion.
- Target application is remote sensing analysis with multimodal data.
- Missing modalities can result from sensor failures, adverse atmospheric conditions, or data acquisition problems.
- The paper is categorized as a cross-type announcement.
- The method aims to improve performance when one or more modalities are unavailable.
Entities
Institutions
- arXiv