Learning to detect real-world anomalous events using video-level annotations
is a difficult task mainly because of the noise present in labels. An anomalous
labelled video may actually contain anomaly only in a short duration while the
rest of the video can be normal. In the current work, we formulate a weakly
supervised anomaly detection method that is trained using only video-level
labels. To this end, we propose to utilize binary clustering which helps in
mitigating the noise present in the labels of anomalous videos. Our formulation
encourages both the main network and the clustering to complement each other in
achieving the goal of weakly supervised training. The proposed method yields
78.27% and 84.16% frame-level AUC on UCF-crime and ShanghaiTech datasets
respectively, demonstrating its superiority over existing state-of-the-art
algorithms.

本文提出了一种利用二进制聚类方法的弱监督异常事件检测方法，在 UCF-crime 和 ShanghaiTech 两个数据集上展示了比现有算法更优异的 78.27％和 84.16％的帧级 AUC。

通过聚类清洗标签噪声以实现最小监督异常检测

Cleaning Label Noise with Clusters for Minimally Supervised Anomaly  Detection

Action recognition in videos has attracted a lot of attention in the past
decade. In order to learn robust models, previous methods usually assume videos
are trimmed as short sequences and require ground-truth annotations of each
video frame/sequence, which is quite costly and time-consuming. In this paper,
given only video-level annotations, we propose a novel weakly supervised
framework to simultaneously locate action frames as well as recognize actions
in untrimmed videos. Our proposed framework consists of two major components.
First, for action frame localization, we take advantage of the self-attention
mechanism to weight each frame, such that the influence of background frames
can be effectively eliminated. Second, considering that there are trimmed
videos publicly available and also they contain useful information to leverage,
we present an additional module to transfer the knowledge from trimmed videos
for improving the classification performance in untrimmed ones. Extensive
experiments are conducted on two benchmark datasets (i.e., THUMOS14 and
ActivityNet1.3), and experimental results clearly corroborate the efficacy of
our method.

提出一种新的基于弱监督框架，通过利用注意力机制定位动作帧同时识别未修剪视频中的动作，利用修剪视频中的信息传递知识来提高分类性能。在 THUMOS14 和 ActivityNet1.3 上经过广泛的实验，实验证明了该方法的有效性。