IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion

• URL: http://arxiv.org/abs/2607.19032v1
• Date: Tue, 21 Jul 2026 12:19:33 GMT
• Title: IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusion
• Abstract summary: Existing Multi-view Anomaly Detection (MAD) methods assume that all views are completely available and model each view separately.<n>We propose textbfIMMoE, which fuses multi-view information through a multi-view expert network and guides the reconstruction of a single view.<n>Our method achieves state-of-the-art performance on both the RIMAD and Real-IAD datasets.
• Score: 13.58401394704324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection [32.33973839948303]
  We present GLAD(Global-Local Attention Driven framework), the first framework combining local and global cross-view fusion for anomaly detection.<n>We show that effective multi-view fusion must explicitly restrict the information reaching the decoder.<n>Experiments on Real-IAD and MANTA-Tiny show that GLAD outperforms state-of-the-art methods across sample-, image-, and pixel-level metrics.
  arXiv  Detail & Related papers  (2026-08-25T21:29:35Z)
• INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model [71.50973774576431]
  We propose a novel MLLM, INF-LLaVA, designed for effective high-resolution image perception. We introduce a Dual-perspective Cropping Module (DCM), which ensures that each sub-image contains continuous details from a local perspective. Second, we introduce Dual-perspective Enhancement Module (DEM) to enable the mutual enhancement of global and local features.
  arXiv  Detail & Related papers  (2024-07-23T06:02:30Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest [65.48057241587398]
  PoIFusion is a framework to fuse information of RGB images and LiDAR point clouds at the points of interest (PoIs) Our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and computation. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach.
  arXiv  Detail & Related papers  (2024-03-14T09:28:12Z)
• Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling [25.02446577349165]
  Multi-view or even multi-modal data is appealing yet challenging for real-world applications. We propose dPoE, a novel multi-view variational autoencoder model that involves (1) a Product-of-Experts layer in tackling multi-view data, (2) a Total Correction discriminator in disentangling view-common and view-specific representations, and (3) a joint loss function in wrapping up all components.
  arXiv  Detail & Related papers  (2023-10-28T15:14:43Z)
• MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes [62.20046129613934]
  We propose a novel multi-view fusion framework, namely multi-view MRD network (MMRDN) We project the 2D data from different views into a common hidden space and fit the embeddings with a set of Von-Mises-Fisher distributions. We select a set of $K$ Maximum Vertical Neighbors (KMVN) points from the point cloud of each object pair, which encodes the relative position of these two objects.
  arXiv  Detail & Related papers  (2023-04-25T05:55:29Z)
• Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images [6.817740582240199]
  Multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. The issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification.
  arXiv  Detail & Related papers  (2023-01-02T12:27:55Z)
• Multiview Detection with Feature Perspective Transformation [59.34619548026885]
  We propose a novel multiview detection system, MVDet. We take an anchor-free approach to aggregate multiview information by projecting feature maps onto the ground plane. Our entire model is end-to-end learnable and achieves 88.2% MODA on the standard Wildtrack dataset.
  arXiv  Detail & Related papers  (2020-07-14T17:58:30Z)
• Generative Partial Multi-View Clustering [133.36721417531734]
  We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
  arXiv  Detail & Related papers  (2020-03-29T17:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.