用动态提问让小模型实现顶尖视频异常检测，无需训练。
QVAD: A Question-Centric Agentic Framework for Efficient and Training-Free Video Anomaly Detection
- 以问答对话方式迭代优化提示，动态引导模型
- 在UCF-Crime等数据集上达顶尖性能，参数量仅为对手几分之一
- 适合边缘设备部署，推理快、内存占用极低
视频异常检测（VAD）是计算机视觉中的基础挑战，尤其因异常的开放集特性而困难。尽管近期基于视觉语言模型（VLM）的无训练方法已显潜力，但通常依赖庞大且资源密集的基础模型来弥补静态提示的模糊性。我们认为，瓶颈不在于模型容量，而在于查询方式的静态性。为此提出QVAD——一种以问题为中心的智能体框架，将VLM与大语言模型（LLM）交互视为动态对话。通过根据视觉上下文迭代优化查询，我们的LLM智能体引导小型VLM生成高保真描述并进行精准语义推理，无需参数更新。这种“提示更新”机制有效激活了轻量级模型的潜在能力，在UCF-Crime、XD-Violence和UBNormal上达到当前最优性能，仅需竞争方法所需参数的一小部分。我们进一步在单场景复杂数据集ComplexVAD上验证其卓越泛化能力。关键的是，QVAD具备高速推理与极小内存占用，使先进VAD能力可部署于资源受限的边缘设备。
原文摘要 · Abstract (English)
Video Anomaly Detection (VAD) is a fundamental challenge in computer vision, particularly due to the open-set nature of anomalies. While recent training-free approaches utilizing Vision-Language Models (VLMs) have shown promise, they typically rely on massive, resource-intensive foundation models to compensate for the ambiguity of static prompts. We argue that the bottleneck in VAD is not necessarily model capacity, but rather the static nature of inquiry. We propose QVAD, a question-centric agentic framework that treats VLM-LLM interaction as a dynamic dialogue. By iteratively refining queries based on visual context, our LLM agent guides smaller VLMs to produce high-fidelity captions and precise semantic reasoning without parameter updates. This ``prompt-updating" mechanism effectively unlocks the latent capabilities of lightweight models, enabling state-of-the-art performance on UCF-Crime, XD-Violence, and UBNormal using a fraction of the parameters required by competing methods. We further demonstrate exceptional generalizability on the single-scene ComplexVAD dataset. Crucially, QVAD achieves high inference speeds with minimal memory footprints, making advanced VAD capabilities deployable on resource-constrained edge devices.
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