# Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

## TL;DR

This paper introduces a graph convolutional network to clean noisy labels in weakly supervised video anomaly detection, enabling the use of fully supervised classifiers and achieving high accuracy across multiple datasets.

## Contribution

It proposes a novel label noise cleaning method using graph convolutional networks, improving weakly supervised anomaly detection performance.

## Key findings

- Achieves 82.12% frame-level AUC on UCF-Crime
- Effective label correction improves classifier accuracy
- Works across different datasets and classifiers

## Abstract

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.