2 citations · 3 across the 3 of their papers we have counts for
6 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…
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…
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…
DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Bicheng Guo, Shuxuan Guo, Miaojing Shi +4
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor a…