MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
This work addresses the problem of detecting anomalies in videos with weak supervision, which is incremental as it builds on existing methods by improving representation learning.
The paper tackles weakly supervised video anomaly detection by proposing a multiple instance self-training framework (MIST) to refine discriminative representations using only video-level annotations, achieving a frame-level AUC of 94.83% on the ShanghaiTech dataset.
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST)to efficiently refine task-specific discriminative representations with only video-level annotations. In particular, MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder that aims to automatically focus on anomalous regions in frames while extracting task-specific representations. Moreover, we adopt a self-training scheme to optimize both components and finally obtain a task-specific feature encoder. Extensive experiments on two public datasets demonstrate the efficacy of our method, and our method performs comparably to or even better than existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.