32 citations · 37 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…