发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对统一异常检测中高维token空间EBM训练不稳定问题，提出ReFP-AD方法，经实验在MVTec-AD和VisA数据集上取得优于基线的异常检测性能。
AI 中文摘要
统一异常检测需要在无异常样本的情况下对高度异质的正常数据进行建模。尽管DINOv2等基础模型能提供丰富的 token 表示，但利用这些空间进行显式密度估计仍具挑战。基于能量的模型（EBM）提供了原理性框架，但其在高维 token 空间中的训练因各向异性和强跨维度相关性而不稳定，会降低有限步马尔可夫链蒙特卡洛（MCMC）采样的性能。我们将此不稳定性归为本质几何问题，提出ReFP-AD（用于异常检测的整流流预处理），其学习几何重参数化，通过最优传输（OT）耦合的整流流将高维嵌入映射到条件良好的潜在空间。该预处理使完整维度 token 空间中基于预处理随机梯度朗之万动力学（SGLD）的持久对比散度得以稳定执行。异常分数随后通过梯度范数从学习到的能量景观中推导得出。在MVTec-AD和VisA数据集的严格统一协议下，ReFP-AD在MVTec-AD上实现98.6%的图像AUROC、97.9%的像素AUROC，在VisA上实现97.3%的图像AUROC、99.0%的像素AUROC，在图像AUROC上较先前的统一EBM基线最高提升10.8%。 ablation实验表明，几何重参数化对有限步MCMC和高维token空间中的准确异常定位至关重要。代码可访问this https URL
英文摘要
Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples. While foundation models like DINOv2 provide rich token representations, leveraging these spaces for explicit density estimation remains challenging. Energy-Based Models (EBMs) offer a principled formulation, but their training in high-dimensional token spaces is unstable due to anisotropy and strong cross-dimensional correlations, which degrades finite-step Markov Chain Monte Carlo (MCMC) sampling. We identify this instability as fundamentally geometric and introduce ReFP-AD (Rectified Flow Preconditioning for Anomaly Detection), which learns a geometric reparameterization that maps high-dimensional embeddings into a well-conditioned latent space via an optimal transport (OT)-coupled rectified flow. This preconditioning enables stable persistent contrastive divergence with preconditioned Stochastic Gradient Langevin Dynamics (SGLD) in full-dimensional token spaces. Anomaly scores are then derived from the learned energy landscape using gradient norms. Under a strict unified protocol on the MVTec-AD and VisA datasets, ReFP-AD achieves 98.6%/97.9% Image/Pixel AUROC on MVTec-AD and 97.3%/99.0% on VisA, outperforming prior unified EBM baselines by up to +10.8% in Image AUROC. Ablation experiments demonstrate that geometric reparameterization is critical for finite-step MCMC and accurate anomaly localization in high-dimensional token spaces. Code is available at https://github.com/CLendering/ReFP-AD
CommentsAccepted at the European Conference on Computer Vision (ECCV) 2026
