Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection

• URL: http://arxiv.org/abs/2503.21125v2
• Date: Fri, 28 Mar 2025 05:51:27 GMT
• Title: Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
• Abstract summary: We build a framework, Omni-AD, to learn normal patterns of different granularity and reconstruct them progressively.<n>Experiments on public anomaly detection benchmarks show that our method outperforms state-of-the-art approaches in MUAD.
• Score: 14.683307311332195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-class unsupervised anomaly detection(MUAD), reconstruction-based methods learn to map input images to normal patterns to identify anomalous pixels. However, this strategy easily falls into the well-known "learning shortcut" issue when decoders fail to capture normal patterns and reconstruct both normal and abnormal samples naively. To address that, we propose to learn the input features in global and local manners, forcing the network to memorize the normal patterns more comprehensively. Specifically, we design a two-branch decoder block, named Omni-block. One branch corresponds to global feature learning, where we serialize two self-attention blocks but replace the query and (key, value) with learnable tokens, respectively, thus capturing global features of normal patterns concisely and thoroughly. The local branch comprises depth-separable convolutions, whose locality enables effective and efficient learning of local features for normal patterns. By stacking Omni-blocks, we build a framework, Omni-AD, to learn normal patterns of different granularity and reconstruct them progressively. Comprehensive experiments on public anomaly detection benchmarks show that our method outperforms state-of-the-art approaches in MUAD. Code is available at https://github.com/easyoo/Omni-AD.git

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)
• One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection [65.11602552904456]
  Universal visual anomaly detection (AD) aims to identify anomaly images and segment anomaly regions towards open and dynamic scenarios.<n>Current methods often struggle with complex prompt engineering, elaborate adaptation modules, and challenging training strategies.<n>This paper presents an embarrassingly simple, general, and effective framework for Universal vision Anomaly Detection (UniADet)
  arXiv  Detail & Related papers  (2026-01-09T06:05:18Z)
• ShortcutBreaker: Low-Rank Noisy Bottleneck with Global Perturbation Attention for Multi-Class Unsupervised Anomaly Detection [59.89803740308262]
  ShortcutBreaker is a novel unified feature-reconstruction framework for MUAD tasks.<n>It features two key innovations to address the issue of shortcuts.<n>The proposed method achieves a remarkable image-level AUROC of 99.8%, 98.9%, 90.6%, and 87.8% on four datasets.
  arXiv  Detail & Related papers  (2025-10-21T06:51:30Z)
• CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection [34.675120608542265]
  We propose a novel approach that leverages class-agnostic learnable prompts to guide the decoded features towards a normal textual representation.<n>Our method achieves competitive performance on the MVTec AD and VisA datasets, demonstrating its effectiveness.
  arXiv  Detail & Related papers  (2024-12-31T08:43:44Z)
• ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning [52.60434474638983]
  We propose a unified framework named ROG$_PL$ to achieve robust open-set learning on complex noisy graph data. The framework consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. To the best of our knowledge, the proposed ROG$_PL$ is the first robust open-set node classification method for graph data with complex noise.
  arXiv  Detail & Related papers  (2024-02-28T17:25:06Z)
• Continuous Memory Representation for Anomaly Detection [24.58611060347548]
  CRAD is a novel anomaly detection method for representing normal features within a "continuous" memory. In an evaluation using the MVTec AD dataset, CRAD significantly outperforms the previous state-of-the-art method by reducing 65.0% of the error for multi-class unified anomaly detection.
  arXiv  Detail & Related papers  (2024-02-28T12:38:44Z)
• Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning [61.57383634677747]
  Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Here, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation) Notably, the proposed algorithm improves the detection performance (up to 5.89% AUC gain) compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-12T08:06:04Z)
• LGN-Net: Local-Global Normality Network for Video Anomaly Detection [9.578697308678153]
  Video detection (VAD) has been intensively studied for years of its potential applications in intelligent video systems unsupervised. Existing VAD methods tend to learn normality from training sets consisting of only normal videos. We propose a two-branch model Local Normality Network (LG-Net) to learn local and global normality simultaneously.
  arXiv  Detail & Related papers  (2022-11-14T15:32:08Z)
• Distilling Object Detectors With Global Knowledge [30.67375886569278]
  Existing methods regard the knowledge as the feature of each instance or their relations, which is the instance-level knowledge only from the teacher model. A more intrinsic approach is to measure the representations of instances w.r.t. a group of common basis vectors in the two feature spaces of the teacher and the student detectors. We show that our method achieves the best performance for distilling object detectors with various datasets backbones.
  arXiv  Detail & Related papers  (2022-10-17T12:44:33Z)
• Style Interleaved Learning for Generalizable Person Re-identification [69.03539634477637]
  We propose a novel style interleaved learning (IL) framework for DG ReID training. Unlike conventional learning strategies, IL incorporates two forward propagations and one backward propagation for each iteration. We show that our model consistently outperforms state-of-the-art methods on large-scale benchmarks for DG ReID.
  arXiv  Detail & Related papers  (2022-07-07T07:41:32Z)
• Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification [2.294635424666456]
  We propose the Cross-modal Local Shortest Path and Global Enhancement (CM-LSP-GE) modules,a two-stream network based on joint learning of local and global features. The experimental results on two typical datasets show that our model is obviously superior to the most state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T10:27:22Z)
• Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection [54.76993389109327]
  Unsupervised anomaly detection aims to build models to detect unseen anomalies by only training on the normal data. We propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges. AMSL incorporates a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations.
  arXiv  Detail & Related papers  (2022-01-03T03:40:21Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios [0.9711326718689492]
  This work proposes a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) for open set recognition of traffic scenarios. By inheriting the ensemble nature of RF, the vote pattern of all trees combined with extreme value theory is shown to be well suited for detecting unknown classes.
  arXiv  Detail & Related papers  (2021-05-17T06:48:15Z)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [59.719925639875036]
  We propose a framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects.
  arXiv  Detail & Related papers  (2021-04-08T19:04:55Z)
• Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches [52.67723703088284]
  We propose a novel framework called multi-patch generative adversarial nets (MPGAN) MPGAN synthesises local patch features and labels unseen classes with a novel weighted voting strategy. MPGAN has significantly greater accuracy than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-27T05:49:44Z)

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.