用图文提示联合建模正常模式，提升无监督异常检测精度
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
- 构建多模态提示记忆库，持续学习视觉与文本中的正常特征
- 在MVTec AD和VisA上实现图像级AUROC和像素级AUPR的SOTA表现
- 适合需要高鲁棒性异常检测的工业质检场景
无监督连续异常检测（UCAD）因能缓解传统方法的灾难性遗忘和计算负担问题而受到关注。然而，仅依赖视觉信息的现有方法难以捕捉复杂场景中正常的流形结构，限制了检测精度的提升。为此，我们提出一种基于多模态提示的无监督持续异常检测框架。具体地，引入持续多模态提示记忆库（CMPMB），从视觉与文本域中逐步提取并保留典型正常模式，形成更丰富的正常表征。同时，设计缺陷语义引导的自适应融合机制（DSG-AFM），结合自适应归一化模块（ANM）与动态融合策略（DFS），协同提升检测精度与对抗鲁棒性。在MVTec AD和VisA数据集上的基准实验表明，该方法在图像级AUROC和像素级AUPR指标上达到当前最优性能。
原文摘要 · Abstract (English)
Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Unsupervised Anomaly Detection (UAD). However, existing UCAD approaches that rely solely on visual information are insufficient to capture the manifold of normality in complex scenes, thereby impeding further gains in anomaly detection accuracy. To overcome this limitation, we propose an unsupervised continual anomaly detection framework grounded in multimodal prompting. Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains prototypical normal patterns from both visual and textual domains across consecutive tasks, yielding a richer representation of normality. Furthermore, we devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness. Benchmark experiments on MVTec AD and VisA datasets show that our approach achieves state-of-the-art (SOTA) performance on image-level AUROC and pixel-level AUPR metrics.
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