Multi-Modal Anomaly Detection: A Survey

2026-08-23

AI Summary

本文从假设驱动的角度综述了多模态异常检测问题，通过正常性假设和异常性假设两种方法解决跨数据源的罕见异常事件检测。

Abstract

Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as industrial inspection and cybersecurity. Yet the literature is fragmented across domains and modality combinations, and existing surveys usually group methods by architecture rather than by how abnormality is defined and separated in multi-modal settings. We survey MMAD from an assumption-driven perspective. We formalize the problem, identify five intrinsic characteristics underlying its core challenges, and organize prior work into two complementary paradigms. The first, normality-assumption methods, models regularity via representation learning, cross-modal alignment, and knowledge enhancement. The second, anomaly-assumption methods, sharpens decision boundaries through coarse-grained, structural, and semantic anomaly injection. We also investigate how foundation models are reshaping MMAD through scalable pretraining, flexible cross-modal transfer, and emerging reasoning capabilities. Finally, we compile representative benchmarks and evaluation protocols across domains and highlight open problems and future directions for robust, adaptive, and interpretable MMAD systems.

Problem

• Multi-Modal Anomaly Detection
• heterogeneous data sources
• safety-critical applications

Innovation

• multi-modal anomaly detection
• normality-assumption methods
• anomaly-assumption methods
• foundation models
• cross-modal transfer