Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection

TL;DR

This paper introduces CPL-VAD, a dual-branch framework using cross pseudo labeling for weakly supervised video anomaly detection, combining temporal localization and semantic recognition to improve performance.

Contribution

The paper presents a novel dual-branch framework with cross pseudo labeling that enhances weakly supervised video anomaly detection by integrating temporal and semantic information.

Findings

Achieves state-of-the-art results on XD-Violence and UCF-Crime datasets.

Effectively combines temporal localization with semantic recognition.

Demonstrates superior performance in anomaly detection and category classification.

Abstract

Weakly supervised video anomaly detection aims to detect anomalies and identify abnormal categories with only video-level labels. We propose CPL-VAD, a dual-branch framework with cross pseudo labeling. The binary anomaly detection branch focuses on snippet-level anomaly localization, while the category classification branch leverages vision-language alignment to recognize abnormal event categories. By exchanging pseudo labels, the two branches transfer complementary strengths, combining temporal precision with semantic discrimination. Experiments on XD-Violence and UCF-Crime demonstrate that CPL-VAD achieves state-of-the-art performance in both anomaly detection and abnormal category classification.