AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion
Julian Wyatt +3 more
semanticscholar +1 more source
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning [PDF]
IEEE International Conference on Computer Vision, 2021 Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets ...
Yu Tian +5 more
semanticscholar +1 more source
Multimodal Industrial Anomaly Detection via Hybrid Fusion [PDF]
Computer Vision and Pattern Recognition, 2023 2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Yue Wang +5 more
semanticscholar +1 more source
Pose‐driven human activity anomaly detection in a CCTV‐like environment
IET Image Processing, 2023 Human activity anomaly detection plays a crucial role in the next generation of surveillance and assisted living systems. Most anomaly detection algorithms are generative models and learn features from raw images.
Yuxing Yang +2 more
doaj +1 more source
Anomaly Detection Based on Indicators Aggregation [PDF]
, 2014 Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential.
Cottrell, Marie +3 more
core +3 more sources
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021 Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data.
Denis A. Gudovskiy +2 more
semanticscholar +1 more source
Improving SIEM for critical SCADA water infrastructures using machine learning [PDF]
, 2019 Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems.
A Bujari +17 more
core +9 more sources
KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [PDF]
Jisuanji kexueTime series data is widely present in fields such as finance,healthcare,industry,and transportation.Time Series Ano-maly Detection(TSAD) is crucial for ensuring system stability and safety.Most current time series anomaly detection methods are ...
WANG Cheng, JIN Cheng
doaj +1 more source
Real-time Anomaly Detection Framework via System Calls Based on Integrated Learning [PDF]
Jisuanji gongcheng, 2023 Anomaly detection based on system calls data cannot complete the synchronous perception task of intrusion behavior within the process lifecycle，and there is a problem of low real-time anomaly detection accuracy.
CHEN Zhonglei, YI Peng, CHEN Xiang, HU Tao
doaj +1 more source
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection [PDF]
Computer Vision and Pattern RecognitionSelf-supervised feature reconstruction methods have shown promising advances in industrial image anomaly de-tection and localization. Despite this progress, these meth-ods still face challenges in synthesizing realistic and di-verse anomaly samples, as ...
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
semanticscholar +1 more source