面向真实世界物理动力学的视觉判别与推理：物理接地异常检测
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection
AI总结：
针对现有工业异常检测缺乏物理推理的问题，提出物理接地异常检测数据集Phys-AD及PAEval指标，以推动真实世界动态场景下的异常检测与解释。
摘要：
人类通过感知、交互并基于对象条件化的物理知识进行推理，从而检测真实世界中的对象异常。工业异常检测（IAD）的长期目标是使机器能够自主复制这一技能。然而，当前的IAD算法主要是在静态、语义简单的数据集上开发和测试，这与需要物理理解和推理的真实世界场景存在差异。为弥合这一差距，我们引入了物理异常检测（Phys-AD）数据集，这是首个大规模、真实世界、物理接地的视频数据集，用于工业异常检测。该数据集使用真实机器人手臂和电机收集，提供了多样化的动态、语义丰富场景。数据集包含超过6400个视频，涵盖22个真实世界对象类别，与机器人手臂和电机交互，并展现出47种异常类型。Phys-AD中的异常检测需要视觉推理，结合物理知识和视频内容来确定对象异常。我们在三种设置下对最先进的异常检测方法进行了基准测试：无监督AD、弱监督AD和视频理解AD，突出了它们在处理物理接地异常方面的局限性。此外，我们引入了物理异常解释（PAEval）指标，旨在评估视觉-语言基础模型不仅检测异常，还能对其潜在物理原因提供准确解释的能力。我们的项目可在https://guyao2023.github.io/Phys-AD/获取。
英文摘要：
Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detection (IAD) is to enable machines to autonomously replicate this skill. However, current IAD algorithms are largely developed and tested on static, semantically simple datasets, which diverge from real-world scenarios where physical understanding and reasoning are essential. To bridge this gap, we introduce the Physics Anomaly Detection (Phys-AD) dataset, the first large-scale, real-world, physics-grounded video dataset for industrial anomaly detection. Collected using a real robot arm and motor, Phys-AD provides a diverse set of dynamic, semantically rich scenarios. The dataset includes more than 6400 videos across 22 real-world object categories, interacting with robot arms and motors, and exhibits 47 types of anomalies. Anomaly detection in Phys-AD requires visual reasoning, combining both physical knowledge and video content to determine object abnormality. We benchmark state-of-the-art anomaly detection methods under three settings: unsupervised AD, weakly-supervised AD, and video-understanding AD, highlighting their limitations in handling physics-grounded anomalies. Additionally, we introduce the Physics Anomaly Explanation (PAEval) metric, designed to assess the ability of visual-language foundation models to not only detect anomalies but also provide accurate explanations for their underlying physical causes. Our project is available at https://guyao2023.github.io/Phys-AD/.
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。
