Weihua Ou — accepted AI-conference papers

2026

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Jiazhen Chen, Xiuqin Liang, Sichao Fu, Zheng Ma, Weihua Ou

AAAI 2026technical

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years. It aims to identify anomalous data patterns using only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically assume complete node attributes and struc

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2019

CAN: Contextual Aggregating Network for Semantic Segmentation

Dechun Cong, Quan Zhou, Jie Cheng, Xiaofu Wu, Suofei Zhang, Weihua Ou +1

ICASSP 2019accepted

Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating network(CAN), a generic convolutional featur…

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