9 citations · 9 across the 3 of their papers we have counts for
5 papers
Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection
Xiaohui Zhou, Yijie Wang, Hongzuo Xu +2
Time Series Foundation Models (TSFMs) achieve strong generalization by learning to reconstruct or forecast broad temporal patterns from large-scale time series during pre-training.…
SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation
Qizhou Wang, Guansong Pang, Christopher Leckie
We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response to recent advances in open-ac…
IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
Xiaohui Zhou, Yijie Wang, Hongzuo Xu +3
Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies…
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
Jiangning Zhang, Chengjie Wang, Xiangtai Li +5
Anomaly detection (AD) is often focused on detecting anomaly areas for industrial quality inspection and medical lesion examination. However, due to the specific scenario targets,…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…