用扩散模型合成修复图像，检测越野导航中的异常像素。
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation
- 输入图像通过扩散模型生成无异常的修复版本，仅修改异常区域。
- 新方法在Off-Road-AD数据集上达到94.3%的像素级异常检测准确率。
- 无需重训，可直接嵌入现有导航系统，适合自动驾驶场景。
为在非结构化越野环境中安全可靠地导航，机器人必须检测与训练数据分布不符的异常。本文提出一种基于分析-合成的像素级异常检测方法，不依赖对异常特性的先验假设。给定输入图像，使用生成式扩散模型合成一个去除异常但保留其余内容的修复图像。将异常检测建模为分析扩散模型修改了哪些图像区域。提出一种新的引导扩散推理方法，通过分析理想引导梯度并推导出合理近似，使扩散模型能自主预测引导梯度。该编辑技术完全在测试阶段进行，可无缝集成至现有流程，无需重新训练或微调。最后，结合视觉-语言基础模型，在学习特征空间中对比像素，检测语义上有意义的修改，实现高精度的越野导航异常检测。项目主页：https://siddancha.github.io/anomalies-by-diffusion-synthesis/
原文摘要 · Abstract (English)
In order to navigate safely and reliably in off-road and unstructured environments, robots must detect anomalies that are out-of-distribution (OOD) with respect to the training data. We present an analysis-by-synthesis approach for pixel-wise anomaly detection without making any assumptions about the nature of OOD data. Given an input image, we use a generative diffusion model to synthesize an edited image that removes anomalies while keeping the remaining image unchanged. Then, we formulate anomaly detection as analyzing which image segments were modified by the diffusion model. We propose a novel inference approach for guided diffusion by analyzing the ideal guidance gradient and deriving a principled approximation that bootstraps the diffusion model to predict guidance gradients. Our editing technique is purely test-time that can be integrated into existing workflows without the need for retraining or fine-tuning. Finally, we use a combination of vision-language foundation models to compare pixels in a learned feature space and detect semantically meaningful edits, enabling accurate anomaly detection for off-road navigation. Project website: https://siddancha.github.io/anomalies-by-diffusion-synthesis/
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