发表机构
South China University of Technology（华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究不完整多视图异常检测问题，提出IMMoE方法，含多视图专家融合与局部异常增强编码器两个关键模块，通过自动生成数据集，在RIMAD和Real-IAD数据集上取得领先性能，提升了像素级和图像级指标。
AI 中文摘要
现有的多视图异常检测（MAD）方法假定所有视图完全可用并分别对每个视图建模。然而，在实际工业场景中，由于遮挡等故障，视图中的信息可能缺失，导致现有方法因缺乏多视图一致性先验而性能下降。为解决此问题，我们探索了更具挑战性的任务：不完整多视图异常检测（IMVAD），其中每个视图的某些区域被屏蔽。我们提出了一个自动生成IMVAD数据集的管道，并通过此管道基于Real-IAD数据集生成了RIMAD数据集。此外，为在缺少视图信息时有效利用多视图信息，我们提出了IMMoE，它由两个关键模块组成：（1）多视图专家融合（MVEF）通过多视图专家网络有效融合多视图信息并指导单视图重建；（2）局部异常增强编码器（LAEE）通过对局部特征应用随机失活有效防止模型过度拟合屏蔽区域。我们的方法在RIMAD和Real-IAD数据集上均取得了领先性能，特别是在RIMAD上，像素级和图像级指标分别提高了11.8%和2.8%。我们的源代码可在该https网址获取。
英文摘要
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 effectively utilize the information of multiple views in the absence of view information, we propose \textbf{IMMoE}, which consists of two key modules: (1) Multi-View Expert Fusion (MVEF) effectively fuses multi-view information through a multi-view expert network and guides the reconstruction of a single view; (2) Local Anomaly Enhancement Encoder (LAEE) effectively prevents the model from overfitting the mask region by applying dropout to local features. Our method achieves state-of-the-art performance on both the RIMAD and Real-IAD datasets, especially on RIMAD, we have increased the pixel-level and image-level metrics by 11.8\% and 2.8\%, respectively. Our source code is available at https://github.com/HULEI7/IMMoE
CommentsAccepted by ECCV 2026
