面向高效多模态异常检测的互补原型映射
Complementary Prototype Mapping for Efficient Multimodal Anomaly Detection
提出互补原型映射框架，通过共识原型和补充原型动态引导跨模态特征映射，解决无监督多模态异常检测中的映射歧义问题，可复用于工业质检和医学影像的缺陷定位任务。
原文依据 we propose an efficient and effective Complementary Prototype Mapping (CPMAD) framework, which dynamically extracts consensus and supplementary prototypes to serve as complementary priors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态无监督异常检测在鲁棒缺陷定位方面日益受到关注。近期方法依赖于在正常条件下建立跨模态匹配关系，且缺乏显式引导。然而，在实践中，单一模态可能对应另一模态的多种不同表示，这种无条件的映射难以自适应地捕捉这些变化，导致映射歧义，并将多样但正常的变异误判为异常。此外，现有方法推理速度慢、内存开销高，阻碍了其在真实生产线中的部署。为解决这些问题，我们提出了一种高效且有效的互补原型映射（CPMAD）框架，该框架动态提取共识原型和补充原型作为互补先验，从而引导和消歧跨模态映射。该框架包含三个关键组件：（1）共识提取模块（CEM）学习一个动态锚点，将多模态特征转换为无异常的共识原型，以提高跨模态一致性并抑制潜在异常；（2）补充查询模块（SQM）采用互补残差注意力机制来捕捉共识空间与模态特定空间之间的差异，从而探索最具代表性和判别性的线索作为补充原型；（3）互补映射模块自适应地整合两种原型以执行特征映射。大量实验表明，CPMAD不仅在多种工业与医学场景的全数据和少样本设置下均取得优越性能，而且与现有方法相比保持了更快的推理速度和更低的内存消耗。代码：https://github.com/yuanzhao-CVLAB/CPMAD。
英文原文摘要 1877 字符
Multimodal unsupervised anomaly detection has garnered increasing attention for robust defect localization.Recent approaches rely on establishing cross-modal matching relationships under normal conditions without explicit guidance.However, in practice, a single modality may have multiple distinct representations corresponding to another modality, and such unconditional mappings struggle to adaptively capture these variations, resulting in mapping ambiguity and the misclassification of diverse yet normal variations as anomalies.Moreover, existing methods suffer from slow inference speed and high memory overhead, hindering their deployment in real-world production lines.To address these issues, we propose an efficient and effective Complementary Prototype Mapping (CPMAD) framework, which dynamically extracts consensus and supplementary prototypes to serve as complementary priors, thereby guiding and disambiguating cross-modal mappings.The framework comprises three key components:(1) Consensus Extraction Module (CEM) learns a dynamic anchor, transforming multimodal features into anomaly-free consensus prototypes to improve cross-modal consistency and suppress latent anomalies;(2) Supplementary Query Module (SQM) employs a Complementary Residual Attention mechanism to capture the discrepancy between the consensus and modality-specific spaces, thereby exploring the most representative and discriminative cues as supplementary prototypes; and(3) Complementary Mapping Module adaptively integrates both prototypes to perform feature mapping.Extensive experiments demonstrate that CPMAD not only achieves superior performance in both full-data and few-shot settings across diverse industrial and medical scenarios but also maintains faster inference speeds and lower memory consumption compared to existing methods. Code: https://github.com/yuanzhao-CVLAB/CPMAD.