Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention

TL;DR

This paper introduces a novel snippet-level anomalous attention mechanism for weakly-supervised video anomaly detection, improving localization accuracy without relying on pseudo labels, and demonstrating effectiveness on benchmark datasets.

Contribution

It proposes an anomalous attention approach that leverages snippet-level features without pseudo labels, enhancing detection and localization in weakly-supervised settings.

Findings

Improved anomaly localization accuracy on benchmark datasets.

Effective detection of challenging video segments.

Outperforms previous methods in weakly-supervised scenarios.

Abstract

With a focus on abnormal events contained within untrimmed videos, there is increasing interest among researchers in video anomaly detection. Among different video anomaly detection scenarios, weakly-supervised video anomaly detection poses a significant challenge as it lacks frame-wise labels during the training stage, only relying on video-level labels as coarse supervision. Previous methods have made attempts to either learn discriminative features in an end-to-end manner or employ a twostage self-training strategy to generate snippet-level pseudo labels. However, both approaches have certain limitations. The former tends to overlook informative features at the snippet level, while the latter can be susceptible to noises. In this paper, we propose an Anomalous Attention mechanism for weakly-supervised anomaly detection to tackle the aforementioned problems. Our approach takes into account snippet-level encoded features without the supervision of pseudo labels. Specifically, our approach first generates snippet-level anomalous attention and then feeds it together with original anomaly scores into a Multi-branch Supervision Module. The module learns different areas of the video, including areas that are challenging to detect, and also assists the attention optimization. Experiments on benchmark datasets XDViolence and UCF-Crime verify the effectiveness of our method. Besides, thanks to the proposed snippet-level attention, we obtain a more precise anomaly localization.