32 citations · 42 across the 15 of their papers we have counts for
15 papers
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
Hui He, Hezhe Qiao, Yutong Chen +2
Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstrea…
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
Feiyi Chen, Leilei Zhang, Guansong Pang +2
In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at ext…
Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination
Anindya Sundar Das, Guansong Pang, Monowar Bhuyan
Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufact…
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
Sinong Zhao, Wenrui Wang, Hongzuo Xu +5
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration.…
Deep Graph Anomaly Detection: A Survey and New Perspectives
Hezhe Qiao, Hanghang Tong, Bo An +3
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its si…
Cluster-Wide Task Slowdown Detection in Cloud System
Feiyi Chen, Yingying Zhang, Lunting Fan +4
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomal…
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Yutong Chen, Hongzuo Xu, Guansong Pang +3
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is…
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Jiawen Zhu, Guansong Pang
This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from d…
CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning
Yukun Li, Guansong Pang, Wei Suo +6
This paper explores the problem of continual learning (CL) of vision-language models (VLMs) in open domains, where the models need to perform continual updating and inference on a…
Generative Semi-supervised Graph Anomaly Detection
Hezhe Qiao, Qingsong Wen, Xiaoli Li +2
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively ex…
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
Yuting Sun, Guansong Pang, Guanhua Ye +3
The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a…
Open-Vocabulary Video Anomaly Detection
Peng Wu, Xuerong Zhou, Guansong Pang +4
Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal.…
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (r…
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Guansong Pang, Yu Tian +2
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task…
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Feiyi Chen, Zhen Qin, Yingying Zhang +4
Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently.…