English(EN) Multi-Modal Anomaly Detection: A Survey

综述论文详述多模态异常检测方法及未来方向

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

一篇新发表在arXiv上的综述论文详述了多模态异常检测（MMAD）领域，该领域旨在从多样化的数据源中识别罕见的异常事件。论文将现有的MMAD方法归类为两种范式：常态假设方法和异常假设方法。它还探讨了基础模型对MMAD的影响，并概述了未来构建更鲁棒、更可解释系统的研究方向。 AI

影响提供了多模态异常检测的结构化概述，可能指导AI安全和网络安全领域的未来研究和开发。

排序理由该条目是一篇在arXiv上发表的综述论文，详述了一个特定的研究领域。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xudong Mou, Zexin Wu, Chuan Luo, Shiru Chen, Xudong Liu, Chunming Hu, Renyu Yang · 2026-08-27 04:00

多模态异常检测：一项调查

arXiv:2608.24937v1 Announce Type: new 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 lit…