Weakly Supervised Video Anomaly Detection via Anomaly-Guided Learning

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.

Keywords

weakly supervised method / video anomaly detection / prompt-enhanced learning / attention mechanism / anomaly-guided learning / key snippet learning

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