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arXiv Machine Learning By Xudong Mou, Zexin Wu, Chuan Luo, Shiru Chen, Xudong Liu, Chunming Hu, Renyu Yang

Multi-Modal Anomaly Detection: A Survey

Read the original on arXiv Machine Learning →

The paper surveys Multi‑Modal Anomaly Detection (MMAD), a field that identifies rare abnormal events across heterogeneous data sources used in safety‑critical domains like industrial inspection and cybersecurity. It formalizes MMAD, outlines five core characteristics, and categorizes existing methods into normality‑assumption and anomaly‑assumption paradigms, highlighting how foundation models are reshaping the field. The survey also compiles benchmarks, evaluation protocols, and identifies open problems for developing robust, adaptive, and interpretable MMAD systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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