activity
20202025
most citedAnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

32 citations · 44 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…

cs.CV2024★ 2 cited

Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey

Kun Shi, Shibo He, Zhenyu Shi +4

Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information…

cs.CV2024★ 3 cited

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…

eess.SP2024

GesturePrint: Enabling User Identification for mmWave-based Gesture Recognition Systems

Lilin Xu, Keyi Wang, Chaojie Gu +3

The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is…