App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Context-aware object priors and semantic decoupling for weakly supervised video anomaly detection

Yang Hongyu
Xu Wanru
Miao Zhenjiang
Yao Ruiying
School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
DOI: 10.19734/j.issn.1001-3695.2025.12.0529

Abstract

Weakly-supervised video anomaly detection (WS-VAD) integrates CLIP to model anomalous foregrounds and background contexts in a single visual feature, but this setting ignored that object abnormality depends heavily on the surrounding environment. This paper proposed a context-aware object prior and semantic decoupling framework for WS-VAD. Large-scale vision-language models constructed in-domain vocabularies for environments and objects on the target dataset. The CLIP text encoder encoded them into environment-level and object-level semantic priors and injected them into temporal video features to reduce object bias. A semantic decoupling consistency constraint further guided a fine-grained visual-text alignment branch. This constraint encouraged object semantics to explain anomalous events and encouraged background regions to remain consistent with environment semantics, which reduced scene bias. Experiments on UCF-Crime and XD-Violence show that the proposed method improves AUC and AP by 1.19% and 1.62%, respectively, and achieves better detection and localization than recent WS-VAD methods.

Foundation Support

国家自然科学基金资助项目(62576028)
北京市自然科学基金资助项目(4242028)
核电安全技术与装备全国重点实验室开放基金资助项目(SKL-2025-TS-13)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.12.0529
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-29] Accepted Paper

Cite This Article

杨宏宇, 许万茹, 苗振江, 等. 基于上下文感知对象先验与语义解耦的弱监督视频异常检测 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0529. (Yang Hongyu, Xu Wanru, Miao Zhenjiang, et al. Context-aware object priors and semantic decoupling for weakly supervised video anomaly detection [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2025.12.0529. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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