Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection

TL;DR

This work proposes CPL-VAD, a dual-branch framework with cross pseudo labeling that achieves state-of-the-art performance in both anomaly detection and abnormal 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.

Figures (4)

• Figure 1: Cross pseudo labeling framework.
• Figure 2: The framework of CPL-VAD and Consistency-Aware Refinement module.
• Figure 3: Qualitative results of CPL-VAD on XD-Violence.
• Figure 4: Comparison of qualitative results without and with cross pseudo labeling.