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Agentic Ensemble with Vision-Language Models for Visual Anomaly Detection

Shangquan Sun, Wenjun Luo, Xu He, Yoonsoo Lee, Shijian Lu · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28560 · open-access PDF

Chinese

视觉异常检测旨在检测异常对象,而异常样本在模型训练期间往往很少。组装多个预训练的探测器可以通过利用其可转移的图像分数和异常地图来帮助。然而,大多数现有的组合方法都适用于固定合并规则,而保存的预测则在预训练的探测器由于不同的训练目标,预测和视觉代表而明显不同,而这些预测器难以匹配。 为此,我们设计了AnomalyAgent,一个训练后合并框架,用于准确的视觉异常检测。

Machine translation (Meta M2M100); the English original follows.

Abstract

Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training. Assembling multiple pretrained detectors can help by leveraging their transferable image scores and anomaly maps. However, most existing ensemble methods apply fixed fusion rules over saved predictions which struggle when pretrained detectors clearly disagree due to different training objectives, priors, and visual representations. To this end, we design AnomalyAgent, a post-training fusion framework for accurate visual anomaly detection. AnomalyAgent aligns heterogeneous sco

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