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Speeding-up diffusion models for remote sensing semantic segmentation
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DOI：10.1016/j.jag.2025.104636.png)
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Abstract
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• Optimized DDPMs with fewer steps for speeding up remote sensing segmentation. • Progressive Distillation beats other DDPM acceleration methods in segmentation. • Test-time augmentations benefit more Diffusion models than convolutional models. • Accelerated DDPM achieves SOTA building segmentation results 32x faster. • Road segmentation with DDPMs is challenging and requires future research.
Keywords:
Diffusion models
Semantic segmentation
Remote sensing
Test time augmentation
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