4 citations · 9 across the 5 of their papers we have counts for
5 papers
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization
Yixuan Zhou, Xing Xu, Zhe Sun +3
Normalizing flows, a category of probabilistic models famed for their capabilities in modeling complex data distributions, have exhibited remarkable efficacy in unsupervised anomal…
BatchNorm-based Weakly Supervised Video Anomaly Detection
Yixuan Zhou, Yi Qu, Xing Xu +3
In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises…
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection
Yixuan Zhou, Xing Xu, Jingkuan Song +2
Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD ap…
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
Yixuan Zhou, Yi Qu, Xing Xu +1
Class imbalance is a common challenge in real-world recognition tasks, where the majority of classes have few samples, also known as tail classes. We address this challenge with th…
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
Yixuan Zhou, Peiyu Yang, Yi Qu +3
Semi-supervised anomaly detection (SSAD) methods have demonstrated their effectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging few-shot but instructive abno…