Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion
Kai Mao, Yiyang Lian, Yangyang Wang, Meiqin Liu, Nanning Zheng, Ping Wei
Abstract
Anomaly detection has garnered significant attention for its extensive industrial application value. Most existing methods focus on single-view scenarios and fail to detect anomalies hidden in blind spots, leaving a gap in addressing the demands of multi-view detection in practical applications. Ensemble of multiple single-view models is a typical way to tackle the multi-view situation, but it overlooks the correlations between different views. In this paper, we propose a novel multi-view anomaly detection framework, Intra-view Decoupling and Inter-view Fusion (IDIF), to explore correlations among views. Our method contains three key components: 1) a proposed Consistency Bottleneck module extracting the common features of different views through information compression and mutual information maximization; 2) an Implicit Voxel Construction module fusing features of different views with prior knowledge represented in the form of voxels; and 3) a View-wise Dropout training strategy enabling the model to learn how to cope with missing views during test. The proposed IDIF achieves state-of-the-art performance on three datasets. Extensive ablation studies also demonstrate the superiority of our methods.
BibTeX
@article{Mao_Lian_Wang_Liu_Zheng_Wei_2025, title={Unveiling Multi-View Anomaly Detection: Intra-view Decoupling and Inter-view Fusion}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33349}, DOI={10.1609/aaai.v39i12.33349}, abstractNote={Anomaly detection has garnered significant attention for its extensive industrial application value. Most existing methods focus on single-view scenarios and fail to detect anomalies hidden in blind spots, leaving a gap in addressing the demands of multi-view detection in practical applications. Ensemble of multiple single-view models is a typical way to tackle the multi-view situation, but it overlooks the correlations between different views. In this paper, we propose a novel multi-view anomaly detection framework, Intra-view Decoupling and Inter-view Fusion (IDIF), to explore correlations among views. Our method contains three key components: 1) a proposed Consistency Bottleneck module extracting the common features of different views through information compression and mutual information maximization; 2) an Implicit Voxel Construction module fusing features of different views with prior knowledge represented in the form of voxels; and 3) a View-wise Dropout training strategy enabling the model to learn how to cope with missing views during test. The proposed IDIF achieves state-of-the-art performance on three datasets. Extensive ablation studies also demonstrate the superiority of our methods.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mao, Kai and Lian, Yiyang and Wang, Yangyang and Liu, Meiqin and Zheng, Nanning and Wei, Ping}, year={2025}, month={Apr.}, pages={12381-12389} }