Fine-VAD：通过渐进式跨粒度学习实现细粒度视频异常检测
Fine-VAD: Towards Fine-Grained Video Anomaly Detection via Progressive Cross-Granularity Learning
把粗粒度异常检测与细粒度类别标签通过渐进式跨粒度对齐统一到一个框架里，可直接迁移到视频动作细粒度识别、时序事件分类等需要多级监督信号的任务。
原文依据 we propose a progressive cross-granularity learning paradigm that leverages coarse- and fine-grained labels in a complementary manner
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文从细粒度视角研究视频异常检测（VAD），其目标不仅是检测异常事件，还要识别其具体类别。由于每个类别的样本数量有限，现有方法要么无法应对多样上下文下的类内变化，要么难以处理由共享视觉基元导致的类间混淆。为应对这些挑战，我们提出一种渐进式跨粒度学习范式，以互补的方式利用粗粒度和细粒度标签，将表征从通用异常模式逐步精炼至类别特定语义。基于该范式，我们构建了 Fine-VAD，一个渐进式对齐框架，在多个粒度上将视频特征与监督信号进行对齐。在两个基准数据集上的大量实验表明，Fine-VAD 在细粒度异常分类上取得最高 47.7% 的相对提升。值得注意的是，我们的范式可良好地泛化到多种模型架构，为现实世界的细粒度 VAD 提供了一种可适配且有效的解决方案。
英文原文摘要 1130 字符
In this paper, we explore video anomaly detection (VAD) from a fine-grained perspective, which aims not only to detect anomalous events but also to identify their specific categories. Due to the limited number of examples per category, existing methods either fail to handle intra-class variation across diverse contexts or struggle with inter-class confusion caused by shared visual primitives. To address these challenges, we propose a progressive cross-granularity learning paradigm that leverages coarse- and fine-grained labels in a complementary manner to progressively refine representations from generic anomaly patterns to category-specific semantics. Building on this paradigm, we develop Fine-VAD, a progressive alignment framework that aligns video features with supervision signals at multiple granularities. Extensive experiments on two benchmark datasets demonstrate that Fine-VAD achieves up to 47.7% relative improvement in fine-grained anomaly classification. Notably, our paradigm generalizes well across diverse model architectures, offering an adaptable and effective solution for real-world fine-grained VAD.