The goal of weakly supervised video anomaly detection is to learn a detection
model using only video-level labeled data. However, prior studies typically
divide videos into fixed-length segments without considering the complexity or
duration of anomalies. Moreover, these studies usually just detect the most
abnormal segments, potentially overlooking the completeness of anomalies. To
address these limitations, we propose a Dynamic Erasing Network (DE-Net) for
weakly supervised video anomaly detection, which learns multi-scale temporal
features. Specifically, to handle duration variations of abnormal events, we
first propose a multi-scale temporal modeling module, capable of extracting
features from segments of varying lengths and capturing both local and global
visual information across different temporal scales. Then, we design a dynamic
erasing strategy, which dynamically assesses the completeness of the detected
anomalies and erases prominent abnormal segments in order to encourage the
model to discover gentle abnormal segments in a video. The proposed method
obtains favorable performance compared to several state-of-the-art approaches
on three datasets: XD-Violence, TAD, and UCF-Crime. Code will be made available
at this https URL

弱监督视频异常检测中，提出了一种动态擦除网络（DE-Net），能够学习多尺度时空特征，处理异常事件时长变化并评估异常检测的完整性。该方法在 XD-Violence、TAD 和 UCF-Crime 三个数据集上表现良好。

基于多尺度时域特征的动态擦除网络用于弱监督视频异常检测

Dynamic Erasing Network Based on Multi-Scale Temporal Features for  Weakly Supervised Video Anomaly Detection

This paper investigates the performance of diffusion models for video anomaly
detection (VAD) within the most challenging but also the most operational
scenario in which the data annotations are not used. As being sparse, diverse,
contextual, and often ambiguous, detecting abnormal events precisely is a very
ambitious task. To this end, we rely only on the information-rich
spatio-temporal data, and the reconstruction power of the diffusion models such
that a high reconstruction error is utilized to decide the abnormality.
Experiments performed on two large-scale video anomaly detection datasets
demonstrate the consistent improvement of the proposed method over the
state-of-the-art generative models while in some cases our method achieves
better scores than the more complex models. This is the first study using a
diffusion model and examining its parameters' influence to present guidance for
VAD in surveillance scenarios.

本研究探究了扩散模型在不使用数据注释的情况下，用于视频异常检测的性能。实验结果表明，该方法优于现有的生成模型，并且是第一项使用扩散模型并研究其参数影响的研究。

探究无监督视频异常检测的扩散模型

Exploring Diffusion Models for Unsupervised Video Anomaly Detection

This paper addresses the problem of joint detection and recounting of
abnormal events in videos. Recounting of abnormal events, i.e., explaining why
they are judged to be abnormal, is an unexplored but critical task in video
surveillance, because it helps human observers quickly judge if they are false
alarms or not. To describe the events in the human-understandable form for
event recounting, learning generic knowledge about visual concepts (e.g.,
object and action) is crucial. Although convolutional neural networks (CNNs)
have achieved promising results in learning such concepts, it remains an open
question as to how to effectively use CNNs for abnormal event detection, mainly
due to the environment-dependent nature of the anomaly detection. In this
paper, we tackle this problem by integrating a generic CNN model and
environment-dependent anomaly detectors. Our approach first learns CNN with
multiple visual tasks to exploit semantic information that is useful for
detecting and recounting abnormal events. By appropriately plugging the model
into anomaly detectors, we can detect and recount abnormal events while taking
advantage of the discriminative power of CNNs. Our approach outperforms the
state-of-the-art on Avenue and UCSD Ped2 benchmarks for abnormal event
detection and also produces promising results of abnormal event recounting.

该研究解决了视频中异常事件的联合检测和记述问题，通过整合通用 CNN 模型和环境相关的异常检测器，该方法可用于检测和记述异常事件，并在 Avenue 和 UCSD Ped2 基准测试中表现优异。

学习深度通用知识的联合异常事件检测与回溯

Joint Detection and Recounting of Abnormal Events by Learning Deep  Generic Knowledge

Online consumer reviews reflect the testimonials of real people, unlike
advertisements. As such, they have critical impact on potential consumers, and
indirectly on businesses. According to a Harvard study (Luca 2011), +1 rise in
star-rating increases revenue by 5-9%. Problematically, such financial
incentives have created a market for spammers to fabricate reviews, to unjustly
promote or demote businesses, activities known as opinion spam (Jindal and Liu
2008). A vast majority of existing work on this problem have formulations based
on static review data, with respective techniques operating in an offline
fashion. Spam campaigns, however, are intended to make most impact during their
course. Abnormal events triggered by spammers' activities could be masked in
the load of future events, which static analysis would fail to identify. In
this work, we approach the opinion spam problem with a temporal formulation.
Specifically, we monitor a list of carefully selected indicative signals of
opinion spam over time and design efficient techniques to both detect and
characterize abnormal events in real-time. Experiments on datasets from two
different review sites show that our approach is fast, effective, and practical
to be deployed in real-world systems.

本文介绍一种基于时间的方法，对在线消费者评论进行实时监测，并利用精心选择的指示性信号，设计有效的技术来检测和描述不正常事件，该方法适用于两个不同评论网站的数据集，且快速、有效、实用，可在现实世界的系统中部署。