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RelVid: Relational Learning with Vision-Language Models for Weakly Video Anomaly Detection.
Jingxin Wang1,2, Guohan Li1,3, Jiaqi Liu1
1Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Sensors (Basel, Switzerland)
|April 12, 2025
Summary
RelVid enhances weakly supervised video anomaly detection by expanding feature gaps with auxiliary tasks. This novel framework improves accuracy and robustness in identifying abnormal events without frame-level supervision.
Area of Science:
- Computer Vision
- Machine Learning
Background:
- Weakly supervised video anomaly detection is challenging due to the lack of frame-level labels.
- Traditional methods struggle with context-dependent normal/abnormal event boundaries and limited feature representation.
Purpose of the Study:
- To introduce RelVid, a novel framework to improve weakly supervised video anomaly detection.
- To enhance the discriminative power of features by expanding the relative feature gap between classes.
Main Methods:
- RelVid integrates auxiliary tasks: text-based anomaly detection and feature reconstruction learning.
- Incorporates class activation feature learning and a temporal attention module for improved discrimination and sequential analysis.
Main Results:
- RelVid demonstrated superior performance on UCF-Crime and XD-Violence benchmark datasets.
- Achieved state-of-the-art results in detection accuracy and robustness compared to existing methods.
Conclusions:
- RelVid effectively addresses limitations in weakly supervised anomaly detection.
- The framework's auxiliary tasks and components significantly boost performance in complex scenarios.
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