十年视频异常检测：综述与展望
Video Anomaly Detection in 10 Years: A Survey and Outlook
AI总结：
本文综述了近十年基于深度学习的视频异常检测方法，涵盖弱监督、自监督和无监督范式，重点分析核心挑战并探讨视觉语言模型作为特征提取器的潜力，为未来研究提供方向。
摘要：
视频异常检测（VAD）在监控、医疗保健和环境监测等多个领域中具有极其重要的意义。虽然已有大量综述聚焦于传统VAD方法，但它们往往缺乏对具体方法和新兴趋势的深入探讨。本综述探讨基于深度学习的VAD，超越传统的监督训练范式，涵盖新兴的弱监督、自监督和无监督方法。本综述的一个突出特点是考察VAD范式中的核心挑战，包括大规模数据集、特征提取、学习方法、损失函数、正则化以及异常分数预测。此外，本综述还研究了视觉语言模型（VLMs）作为VAD强大特征提取器的潜力。VLMs将视觉数据与视频中的文本描述或口语相结合，能够实现对场景的细致理解，这对异常检测至关重要。通过应对这些挑战并提出未来研究方向，本综述旨在促进稳健且高效的VAD系统的发展，利用VLMs的能力在复杂的现实场景中增强异常检测。这一全面分析旨在弥合现有知识空白，为研究人员提供有价值的见解，并为塑造VAD研究的未来做出贡献。
英文摘要：
Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches. A prominent feature of this review is the investigation of core challenges within the VAD paradigms including large-scale datasets, features extraction, learning methods, loss functions, regularization, and anomaly score prediction. Moreover, this review also investigates the vision language models (VLMs) as potent feature extractors for VAD. VLMs integrate visual data with textual descriptions or spoken language from videos, enabling a nuanced understanding of scenes crucial for anomaly detection. By addressing these challenges and proposing future research directions, this review aims to foster the development of robust and efficient VAD systems leveraging the capabilities of VLMs for enhanced anomaly detection in complex real-world scenarios. This comprehensive analysis seeks to bridge existing knowledge gaps, provide researchers with valuable insights, and contribute to shaping the future of VAD research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
