Chengkun Wu — accepted AI-conference papers

2022

Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement

Guang Yu, Siqi Wang, Zhiping Cai, Xinwang Liu, Chuanfu Xu, Chengkun Wu

CVPR 2022poster

While classic video anomaly detection (VAD) requires labeled normal videos for training, emerging unsupervised VAD (UVAD) aims to discover anomalies directly from fully unlabeled videos. However, existing UVAD methods still rely on shallow models to perform detection or initialization, and they are…

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2022

Stgat-Mad : Spatial-Temporal Graph Attention Network For Multivariate Time Series Anomaly Detection

Jun Zhan, Siqi Wang, Xiandong Ma, Chengkun Wu, Canqun Yang, Detian Zeng +1

ICASSP 2022accepted

Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations. This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGAT-MAD). The core of our framew…

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