Anomalous event detection in surveillance videos is a challenging and
practical research problem among image and video processing community. Compared
to the frame-level annotations of anomalous events, obtaining video-level
annotations is quite fast and cheap though such high-level labels may contain
significant noise. More specifically, an anomalous labeled video may actually
contain anomaly only in a short duration while the rest of the video frames may
be normal. In the current work, we propose a weakly supervised anomaly
detection framework based on deep neural networks which is trained in a
self-reasoning fashion using only video-level labels. To carry out the
self-reasoning based training, we generate pseudo labels by using binary
clustering of spatio-temporal video features which helps in mitigating the
noise present in the labels of anomalous videos. Our proposed formulation
encourages both the main network and the clustering to complement each other in
achieving the goal of more accurate anomaly detection. The proposed framework
has been evaluated on publicly available real-world anomaly detection datasets
including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate
superiority of our proposed framework over the current state-of-the-art
methods.

本研究提出了一种基于深度神经网络的弱监督异常检测框架，采用视频级别的标签，通过使用时空视频特征的二元聚类来产生伪标签，从而减少异常视频标签中存在的噪声，实现更准确的异常检测。

使用视频级标签的自我推理框架进行异常检测

A Self-Reasoning Framework for Anomaly Detection Using Video-Level  Labels

Analysis of opinion dynamics in social networks plays an important role in
today's life. For applications such as predicting users' political preference,
it is particularly important to be able to analyze the dynamics of competing
opinions. While observing the evolution of polar opinions of a social network's
users over time, can we tell when the network "behaved" abnormally?
Furthermore, can we predict how the opinions of the users will change in the
future? Do opinions evolve according to existing network opinion dynamics
models? To answer such questions, it is not sufficient to study individual user
behavior, since opinions can spread far beyond users' egonets. We need a method
to analyze opinion dynamics of all network users simultaneously and capture the
effect of individuals' behavior on the global evolution pattern of the social
network.
In this work, we introduce Social Network Distance (SND) - a distance measure
that quantifies the "cost" of evolution of one snapshot of a social network
into another snapshot under various models of polar opinion propagation. SND
has a rich semantics of a transportation problem, yet, is computable in time
linear in the number of users, which makes SND applicable to the analysis of
large-scale online social networks. In our experiments with synthetic and
real-world Twitter data, we demonstrate the utility of our distance measure for
anomalous event detection. It achieves a true positive rate of 0.83, twice as
high as that of alternatives. When employed for opinion prediction in Twitter,
our method's accuracy is 75.63%, which is 7.5% higher than that of the next
best method.
Source Code: this https URL

介绍了一种新的社交网络距离（SND）测量方法，主要应用于社交网络的观点动态分析（如预测用户的政治倾向），实验结果表明该方法在异常事件检测和观点预测方面具有很高的准确率。

社交网络中极性意见动态分析距离指标

A Distance Measure for the Analysis of Polar Opinion Dynamics in Social  Networks

We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.

通过深度神经网络及自动特征提取技术，提出了一种新的无监督深度学习框架，用于复杂视频场景中的异常事件检测，其中融合了传统早期融合和晚期融合策略，并结合多个一类支持向量机模型进行最终异常检测。