AnomalyCraft-700K：用于细粒度视频异常理解的组件级可控且可验证的合成异常数据集
AnomalyCraft-700K: Component-Level Controllable and Verifiable Synthetic Anomalies for Fine-Grained Video Anomaly Understanding
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中文总结 AI 辅助
提出AnomalyCraft-700K，一个包含4万视频和70万标注的组件可控合成异常数据集，通过三阶段流水线生成细粒度异常事件并验证跨模态对齐，有效提升视频异常理解性能。
中文摘要 AI 辅助
视频异常理解（VAU）的进展长期以来受到真实世界异常视频固有缺陷的限制，这些视频难以收集且对其内容控制有限。合成异常方法在一定程度上缓解了数据稀缺问题，但其生成过程仍主要在类别或提示级别进行控制。这些方法也缺乏视频-文本一致性的组件级验证，并且在正常-异常边界附近提供的困难正常样本不足。为解决这一问题，我们提出了AnomalyCraft-700K，一个用于细粒度VAU的组件可控合成异常数据集，包含超过4万段视频和超过70万条任务级文本标注。通过细粒度语义组件和渐进式三阶段流水线，我们构建了内容丰富、语义可控且具有时间结构的异常事件，并额外构建了每类困难正常样本，以促使模型基于异常语义而非表面视觉线索进行判别。此外，利用组件作为验证单元，AnomalyCraft-700K进一步对生成过程中引入的视频-文本差异进行组件级修正，为从异常检测、经异常检索和描述到细粒度异常推理的六项任务提供具有已验证跨模态对齐的可靠标注。在传统协议和基于多模态大语言模型（MLLM）的协议下对广泛使用方法的评估表明，AnomalyCraft-700K可作为从异常检测到细粒度异常理解的有效监督来源。
英文摘要
Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world anomaly videos, which are hard to collect and offer little control over their content. Synthetic anomaly approaches partially alleviate data scarcity, yet their generation remains largely controlled at the category or prompt level. They also lack component-level verification of video-text consistency and provide insufficient hard normal samples near the normal-anomaly boundary. To address this, we present AnomalyCraft-700K, a component-controllable synthetic anomaly dataset for fine-grained VAU, containing over 40K videos and over 700K task-level textual annotations. From fine-grained semantic components and a progressive three-stage pipeline, we craft anomaly events that are richly detailed, semantically controlled, and temporally structured, and additionally construct per-category hard normal samples to prompt the model to discriminate based on anomaly semantics rather than surface visual cues. Moreover, using the components as verification units, AnomalyCraft-700K further performs component-wise correction of video-text discrepancies introduced during generation, providing reliable annotations with verified cross-modal alignment for six tasks that progress from anomaly detection, through anomaly retrieval and captioning, to fine-grained anomaly reasoning. Evaluations of widely used methods under both traditional and MLLM-based protocols demonstrate that AnomalyCraft-700K serves as an effective source of supervision, from anomaly detection to fine-grained anomaly understanding.
发表机构
- Northwestern Polytechnical University（西北工业大学）
- Singapore Management University（新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。
