基于跨模态融合与双曲图注意力机制的视频异常检测

姜迪; 赖惠成; 汪烈军

doi:10.11959/j.issn.1000-436x.2025110

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基于跨模态融合与双曲图注意力机制的视频异常检测

学术论文 | 更新时间：2025-07-04

- 基于跨模态融合与双曲图注意力机制的视频异常检测
- Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism
- 通信学报 2025年46卷第6期页码：136-152
- 作者机构：
  
  1.新疆大学计算机科学与技术学院，新疆乌鲁木齐 830017
  2.新疆大学新疆维吾尔自治区信号检测与处理重点实验室，新疆乌鲁木齐 830017
  3.丝路多语言认知计算国际合作联合实验室，新疆乌鲁木齐 830017
- 作者简介：
  
  [ "姜迪（1997- ），男，山东济宁人，新疆大学博士生，主要研究方向为视频异常检测、深度学习、目标检测等。" ]
  [ "赖惠成（1963- ），男，四川德阳人，新疆大学教授、博士生导师，主要研究方向为视频/图像信息处理、图像理解与识别等。" ]
  [ "汪烈军（1975- ），男，四川眉山人，博士，新疆大学教授、博士生导师，主要研究方向为视频通信处理、图像识别与处理等。" ]
- 基金信息：
  
  国家自然科学基金联合基金资助项目(U1903213);新疆维吾尔自治区重点研发计划基金资助项目(2022B01008)
- DOI：10.11959/j.issn.1000-436x.2025110
  中图分类号： TP391.41
- 收稿：2025-04-17，
  
  修回：2025-06-04，
  
  纸质出版：2025-06-25
- 稿件说明：
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姜迪,赖惠成,汪烈军.基于跨模态融合与双曲图注意力机制的视频异常检测[J].通信学报,2025,46(06):136-152.

JIANG Di,LAI Huicheng,WANG Liejun.Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism[J].Journal on Communications,2025,46(06):136-152.
姜迪,赖惠成,汪烈军.基于跨模态融合与双曲图注意力机制的视频异常检测[J].通信学报,2025,46(06):136-152. DOI： 10.11959/j.issn.1000-436x.2025110.

JIANG Di,LAI Huicheng,WANG Liejun.Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism[J].Journal on Communications,2025,46(06):136-152. DOI： 10.11959/j.issn.1000-436x.2025110.

摘要

针对视频异常检测中模态信息不平衡、视听噪声不平均以及模态异步等问题，提出了一个动态跨模态融合模块与双曲图注意力机制融合的多模态视频异常检测方法CM-HVAD，以准确检测异常行为。首先，提出了一种新的动态跨模态融合模块，动态压缩多模态数据特征，自主学习跨模态权重，动态平衡视觉特征和音视频特征并进行融合增强。然后，针对多模态数据中存在的模态异步问题，提出了模态一致性对齐模块，按时间帧序列对齐模态语义，确保多模态数据在时间和语义上的一致性。最后，引入了双曲图注意力机制，通过双曲空间的模式分离特性，有效捕捉正常和异常表示之间的层次关系，从而提高检测准确率。实验结果表明，所提方法在XD-Violence上AP达到了86.47%，在UCF-Crime上AUC达到了87.12%，性能优于基线方法。

Abstract

To address the challenges of modality information imbalance

non-uniform audiovisual noise

and modality asynchrony in video anomaly detection

a multimodal video anomaly detection method called CM-HVAD was proposed for accurate anomaly detection. Firstly

a novel dynamic cross-modal fusion module was introduced to dynamically compress and reweight multimodal features through autonomous learning of cross-modal weights

thereby achieving balanced and enhanced fusion of visual and audio features. Secondly

to address the issue of modal asynchrony in multimodal data

a modal consistency alignment module was proposed

which aligned modal semantics along the temporal frame sequence to ensure both temporal and semantic consistency in multimodal data. Finally

a hyperbolic graph attention mechanism was incorporated to effectively capture the hierarchical relationships between normal and abnormal representations through the pattern separation property of hyperbolic space

thereby improving detection accuracy. The results show that the proposed method achieves 86.47% AP on XD-Violence and 87.12% AUC on UCF-Crime

outperforming baseline methods.

关键词

Keywords

references

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