IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion
Existing Multi-view Anomaly Detection (MAD) methods assume that all views are completely available and model each view separately. However, in real industrial scenarios, information in the view may be missing due to faults such as occlusion, which leads to the performance degradation of existing methods due to the lack of a multi-view consistency prior. To address this, we explored a more challenging task: Incomplete Multi-View Anomaly Detection (IMVAD), in which some areas of each view were masked. We proposed a pipeline for automatically generating the IMVAD dataset and generated the \textbf{RIMAD} dataset based on the Real-IAD dataset through this pipeline. In addition, in order to eff...
| Authors | Lei Hu |
|---|---|
| Primary category | cs.CV |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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