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Related Experiment Video

Updated: Jan 9, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Semantic Anchor-Aligned Model for Interpretable Video Anomaly Detection under Cross-Modal Weak Supervision.

Weishan Gao¹, Jiangang Wang², Ye Wang³

¹China Aerospace Academy of Systems Science and Engineering; Aerospace Hongka Intelligent Technology (Beijing) Co., Ltd.; gaowsh518@163.com.

Journal of Visualized Experiments : Jove

|December 1, 2025

Summary

This study introduces a novel feature modeling approach for weakly supervised video anomaly detection, enhancing interpretability by using structured knowledge and semantic guidance. The method achieves high accuracy in detecting and classifying anomalies, offering a more detailed analysis beyond simple binary detection.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Weakly supervised video anomaly detection uses video-level labels for event identification.
Traditional multiple instance learning (MIL) methods offer limited fine-grained anomaly categorization and lack interpretability.
Existing methods often focus on the most anomalous segments, hindering detailed analysis.

Purpose of the Study:

To develop an interpretable weakly supervised video anomaly detection method using structured knowledge.
To enhance the distinction between fine-grained anomaly categories.
To move beyond binary classification towards a semantics-driven anomaly analysis framework.

Main Methods:

A feature modelling approach incorporating structured knowledge is proposed.
A dynamic semantic guidance mechanism combines external category-level information with learnable prompts.
Semantic signals are generated and aligned with visual evidence for anomaly detection and description.

Main Results:

Achieved 88.03% AUC on UCF-Crime and 98.23% AUC on ShanghaiTech datasets.
Attained 87.05% accuracy in fine-grained anomaly classification.
Generated semantic explanations for interpretable anomaly analysis.

Conclusions:

The proposed method successfully addresses limitations of traditional MIL approaches in video anomaly detection.
The integration of structured knowledge and semantic guidance enables more interpretable and fine-grained anomaly analysis.
The framework advances weakly supervised detection towards a semantics-driven, explanatory analysis.