不训练模型也能让火势预测更全面，提升多场景下分割多样性。
Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods
- 用无需训练的采样方法增强扩散模型生成结果多样性
- 在火灾数据集上提升16.4%的分割重合度指标
- 适合需要快速生成多种合理预测的应急决策场景
在不确定环境（如野火蔓延、医疗诊断或自动驾驶）中预测未来状态，需要能考虑多种合理结果的模型。尽管扩散模型能有效学习多模态分布，但直接采样效率低下，可能需数百次采样才能找到低概率但实际重要的模式。本文评估了几种无需训练的采样方法，以提升样本效率下的预测多样性。将原本用于自然图像生成的粒子引导和SPELL方法适配到离散分割任务，并提出一种基于聚类的简单方法。在LIDC医学数据集、改进版Cityscapes数据集及本文提出的模拟火灾蔓延数据集MMFire上验证，相比直接采样，这些方法在MMFire上将HM IoU*提升最多7.5%，在Cityscapes上提升16.4%，证明无需训练即可高效提升分割扩散模型的样本多样性，且对图像质量与运行时间影响极小。代码与数据集见：https://github.com/SebastianGer/wildfire-spread-scenarios
原文摘要 · Abstract (English)
Predicting future states in uncertain environments, such as wildfire spread, medical diagnosis, or autonomous driving, requires models that can consider multiple plausible outcomes. While diffusion models can effectively learn such multi-modal distributions, naively sampling from these models is computationally inefficient, potentially requiring hundreds of samples to find low-probability modes that may still be operationally relevant. In this work, we address the challenge of sample-efficient ambiguous segmentation by evaluating several training-free sampling methods that encourage diverse predictions. We adapt two techniques, particle guidance and SPELL, originally designed for the generation of diverse natural images, to discrete segmentation tasks, and additionally propose a simple clustering-based technique. We validate these approaches on the LIDC medical dataset, a modified version of the Cityscapes dataset, and MMFire, a new simulation-based wildfire spread dataset introduced in this paper. Compared to naive sampling, these approaches increase the HM IoU* metric by up to 7.5% on MMFire and 16.4% on Cityscapes, demonstrating that training-free methods can be used to efficiently increase the sample diversity of segmentation diffusion models with little cost to image quality and runtime. Code and dataset: https://github.com/SebastianGer/wildfire-spread-scenarios
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