东华大学学报（英文版）2026，Vol.43Issue(4)：83-91,9.DOI:10.19884/j.1672-5220.202512010
基于异常引导学习的弱监督视频异常检测
Weakly Supervised Video Anomaly Detection via Anomaly-Guided Learning
摘要
Abstract
Video anomaly detection(VAD)aims to identify snippets or events that deviate from normal behavior in video sequences.With the growing demand for public safety and surveillance,this field has developed rapidly.In recent years,weakly supervised VAD(WSVAD)has attracted increasing attention.However,its performance remains limited due to the challenges of multiple instance learning(MIL),including high dataset complexity and substantial video noise,which severely hinder model learning.To address these issues,we propose a WSVAD method guided by anomaly representations.Specifically,our method leverages anomaly-related representations to correct biases in the learning process.It extracts key snippet and anomaly category representations through the key snippet learning module and anomaly classification module,respectively.These two types of representations are then embedded via an anomaly-guided attention mechanism.Extensive experiments on the ShanghaiTech and UCF-Crime datasets demonstrate that the proposed method significantly improves detection accuracy and achieves competitive performance.关键词
弱监督方法/视频异常检测/提示增强学习/注意力机制/异常引导学习/关键片段学习Key words
weakly supervised method/video anomaly detection/prompt-enhanced learning/attention mechanism/anomaly-guided learning/key snippet learning分类
信息技术与安全科学引用本文复制引用
王执祺,唐雪嵩,郝矿荣..基于异常引导学习的弱监督视频异常检测[J].东华大学学报（英文版）,2026,43(4):83-91,9.基金项目
National Natural Science Foundation of China(No.62176052) （No.62176052）
