32 citations · 37 across the 8 of their papers we have counts for
8 papers
TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Binbin Gao, Guansong Pang +3
Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. However, existing methods typically rely on a single textual space to align…
PointAD+: Learning Hierarchical Representations for Zero-shot 3D Anomaly Detection
Qihang Zhou, Shibo He, Jiangtao Yan +2
In this paper, we aim to transfer CLIP's robust 2D generalization capabilities to identify 3D anomalies across unseen objects of highly diverse class semantics. To this end, we pro…
FairDD: Fair Dataset Distillation
Qihang Zhou, Shenhao Fang, Shibo He +2
Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in…
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection
Qihang Zhou, Jiangtao Yan, Shibo He +2
Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that addresses scenarios where target 3D training samples are unavailable due to practical concerns like priva…
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
Chen Liu, Shibo He, Qihang Zhou +2
Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training…
Label-Free Multivariate Time Series Anomaly Detection
Qihang Zhou, Shibo He, Haoyu Liu +2
Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which i…
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…
Detecting Multivariate Time Series Anomalies with Zero Known Label
Qihang Zhou, Jiming Chen, Haoyu Liu +2
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However,…