Abstract
Video Anomaly Understanding (VAU) is essential for applications such as smart cities, security surveillance, and disaster alert systems, yet remains challenging due to its demand for fine-grained spatio-temporal perception and robust reasoning under ambiguity. Despite advances in anomaly detection, existing methods often lack interpretability and struggle to capture the causal and contextual aspects of abnormal events. This limitation is further compounded by the absence of comprehensive benchmarks for evaluating reasoning ability in anomaly scenarios. To address both challenges, we introduce VAU-R1, a data-efficient framework built upon Multimodal Large Language Models (MLLMs), which enhances anomaly reasoning through Reinforcement Fine-Tuning (RFT). Besides, we propose VAU-Bench, the first Chain-of-Thought benchmark tailored for video anomaly reasoning, featuring multiple-choice QA, detailed rationales, temporal annotations, and descriptive captions. Empirical results show that VAU-R1 significantly improves question answering accuracy, temporal grounding, and reasoning coherence across diverse contexts. Together, our method and benchmark establish a strong foundation for interpretable and reasoning-aware video anomaly understanding. Our code is available at this https URL.
Abstract (translated)
视频异常理解（VAU）对于智能城市、安全监控和灾害预警系统等应用至关重要，但由于其对细粒度时空感知以及在模糊条件下进行稳健推理的需求，这项任务仍然具有挑战性。尽管在异常检测方面有所进步，现有的方法通常缺乏可解释性，并且难以捕捉异常事件的因果关系和上下文背景。这种局限性还因评估异常场景中推理能力的全面基准测试缺失而进一步加剧。为了解决这些挑战，我们引入了VAU-R1，这是一个基于多模态大型语言模型（MLLM）的数据高效框架，通过强化微调（RFT）来增强异常推理。此外，我们提出了VAU-Bench，这是第一个针对视频异常推理的Chain-of-Thought基准测试，其特点包括多项选择题、详细的理由说明、时间标注和描述性字幕。 实证结果显示，VAU-R1在各种上下文中显著提高了问题回答准确率、时间定位精度以及推理一致性。我们的方法与基准测试共同为可解释性和推理意识的视频异常理解奠定了坚实的基础。我们的代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2505.23504
