Výsledky vyhledávání - "Zhou, Qihang"

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PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection

Autor: Zhou, Qihang, Yan, Jiangtao, He, Shibo, Meng, Wenchao, Chen, Jiming

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 privacy protection. This paper introduces PointAD, a novel approach that tr

Externí odkaz: http://arxiv.org/abs/2410.00320

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Single spin asymmetry $ A _{ U L } ^ { \sin ( 2 \phi _ { h } ) }$ in dihadron production in SIDIS

Autor: Yang, Ren, Yu, Yangyang, Zhou, Qihang, Li, Gang, Song, Mao, Luo, Xuan

Publikováno v: Phys. Rev. D 109, 114038 (2024)

The paper calculates the helicity-dependent dihadron fragmentation function (DiFF), by extending the dihadron spectator model and examine the single longitudinal spin asymmetry $A^{\sin(2\phi_h)}_{UL}$ from dihadron in semi-inclusive inelastic scatte

Externí odkaz: http://arxiv.org/abs/2405.19624

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Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection

Autor: Liu, Chen, He, Shibo, Zhou, Qihang, Li, Shizhong, Meng, Wenchao

Self-supervised methods have gained prominence in time series anomaly detection due to the scarcity of available annotations. Nevertheless, they typically demand extensive training data to acquire a generalizable representation map, which conflicts w

Externí odkaz: http://arxiv.org/abs/2401.15123

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Label-Free Multivariate Time Series Anomaly Detection

Autor: Zhou, Qihang, He, Shibo, Liu, Haoyu, Chen, Jiming, Meng, Wenchao

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 is difficult to guarantee in practical situations. Such a case may degr

Externí odkaz: http://arxiv.org/abs/2312.11549

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AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

Autor: Zhou, Qihang, Pang, Guansong, Tian, Yu, He, Shibo, Chen, Jiming

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 when training data is not accessible due to various concerns, eg, data

Externí odkaz: http://arxiv.org/abs/2310.18961

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Detecting Multivariate Time Series Anomalies with Zero Known Label

Autor: Zhou, Qihang, Chen, Jiming, Liu, Haoyu, He, Shibo, Meng, Wenchao

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, preparing such a dataset is very laborious since each single data inst

Externí odkaz: http://arxiv.org/abs/2208.02108

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