Abstract
Weakly supervised video anomaly detection (WS-VAD) is tasked with pinpointing temporal intervals containing anomalous events within untrimmed videos, utilizing only video-level annotations. However, a significant challenge arises due to the absence of dense frame-level annotations, often leading to incomplete localization in existing WS-VAD methods. To address this issue, we present a novel LEC-VAD, Learning Event Completeness for Weakly Supervised Video Anomaly Detection, which features a dual structure designed to encode both category-aware and category-agnostic semantics between vision and language. Within LEC-VAD, we devise semantic regularities that leverage an anomaly-aware Gaussian mixture to learn precise event boundaries, thereby yielding more complete event instances. Besides, we develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories. This innovation bolsters the text's expressiveness, which is crucial for advancing WS-VAD. Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime.
Abstract (translated)
弱监督视频异常检测（WS-VAD）的任务是在未经修剪的视频中识别包含异常事件的时间区间，仅使用视频级别的注释信息。然而，由于缺乏密集的帧级别注释，现有WS-VAD方法通常会遇到定位不完整的问题。为了解决这个问题，我们提出了一种新的LECVAD (Learning Event Completeness for Weakly Supervised Video Anomaly Detection)，该方法采用双层结构来编码视觉和语言之间的类别感知和非类别感知语义信息。在LECVAD中，我们设计了利用异常意识高斯混合体来学习精确事件边界的语义规律，从而提供更完整的事件实例。此外，我们开发了一种基于记忆库的原型学习机制，用于丰富与异常事件类别相关的简洁文本描述。这一创新增强了文本的表现力，在推进WS-VAD方面至关重要。我们的LECVAD在两个基准数据集（XD-Violence和UCF-Crime）上展示了相对于当前最先进方法的重大进步。
URL
https://arxiv.org/abs/2506.13095
