针对视图缺失的异常检测难题，提出多专家融合新方法，性能显著提升。
IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion
- 构建多视图专家融合网络，自动整合不完整视图信息
- 在RIMAD数据集上像素级与图像级指标分别提升11.8%和2.8%
- 适合工业场景中存在遮挡或数据缺失的异常检测任务
现有多视图异常检测方法假设所有视图均完整可用，且独立建模各视图。但在实际工业场景中，因遮挡等故障导致部分视图信息缺失，现有方法因缺乏多视图一致性先验而性能下降。为此，我们提出更具挑战性的不完整多视图异常检测（IMVAD）任务，其中每视图部分区域被掩码。我们设计了自动生成IMVAD数据集的流程，并基于Real-IAD数据集构建了新的RIMAD数据集。针对视图信息缺失问题，提出IMMoE方法，包含两个核心模块：(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丢弃策略，防止模型过拟合掩码区域。IMMoE在RIMAD和Real-IAD数据集上均取得领先性能，尤其在RIMAD上像素级与图像级指标分别提升11.8%和2.8%。代码已开源。
原文摘要 · Abstract (English)
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
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