Blind Localization and Clustering of Anomalies in Textures

• URL: http://arxiv.org/abs/2404.12246v1
• Date: Thu, 18 Apr 2024 15:11:02 GMT
• Title: Blind Localization and Clustering of Anomalies in Textures
• Authors: Andrei-Timotei Ardelean, Tim Weyrich,
• Abstract summary: Anomaly detection and localization in images is a growing field in computer vision. We propose a novel method for clustering anomalies in largely stationary images in a blind setting. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning.
• Score: 2.6117257131764715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection and localization in images is a growing field in computer vision. In this area, a seemingly understudied problem is anomaly clustering, i.e., identifying and grouping different types of anomalies in a fully unsupervised manner. In this work, we propose a novel method for clustering anomalies in largely stationary images (textures) in a blind setting. That is, the input consists of normal and anomalous images without distinction and without labels. What contributes to the difficulty of the task is that anomalous regions are often small and may present only subtle changes in appearance, which can be easily overshadowed by the genuine variance in the texture. Moreover, each anomaly type may have a complex appearance distribution. We introduce a novel scheme for solving this task using a combination of blind anomaly localization and contrastive learning. By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation. Our experiments show that the proposed solution yields significantly better results compared to prior work, setting a new state of the art. Project page: https://reality.tf.fau.de/pub/ardelean2024blind.html.

Related papers

• On The Relationship between Visual Anomaly-free and Anomalous Representations [0.0]
  Anomaly Detection is an important problem within computer vision, having variety of real-life applications. In this paper, we make an important hypothesis and show, by exhaustive experimentation, that the space of anomaly-free visual patterns of the normal samples correlates well with each of the various spaces of anomalous patterns of the class-specific anomaly samples.
  arXiv  Detail & Related papers  (2024-10-09T06:18:53Z)
• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• Alleviating Structural Distribution Shift in Graph Anomaly Detection [70.1022676681496]
  Graph anomaly detection (GAD) is a challenging binary classification problem. Gallon neural networks (GNNs) benefit the classification of normals from aggregating homophilous neighbors. We propose a framework to mitigate the effect of heterophilous neighbors and make them invariant.
  arXiv  Detail & Related papers  (2024-01-25T13:07:34Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection [64.21963650519312]
  Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types.
  arXiv  Detail & Related papers  (2023-10-01T21:24:05Z)
• That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering [28.296651124677556]
  Setting blind anomaly detection (BAD) can be converted into a local outlier detection problem. We propose a novel method named PatchCluster that can accurately detect image- and pixel-level anomalies. Experimental results show that PatchCluster shows a promising performance without the knowledge of normal data.
  arXiv  Detail & Related papers  (2023-07-06T18:17:43Z)
• SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection [24.43321988051129]
  We propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample.
  arXiv  Detail & Related papers  (2023-06-14T08:55:36Z)
• Prototypical Residual Networks for Anomaly Detection and Localization [80.5730594002466]
  We propose a framework called Prototypical Residual Network (PRN) PRN learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. We present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies.
  arXiv  Detail & Related papers  (2022-12-05T05:03:46Z)
• Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks [2.9237210794416755]
  We introduce novel discriminative and generative tasks which focus on different visual cues. We present a new out-of-distribution detection function and highlight its better stability compared to other out-of-distribution detection methods. Our model can more accurately learn highly discriminative features using these self-supervised tasks.
  arXiv  Detail & Related papers  (2021-11-24T09:54:50Z)
• Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization [19.23452967227186]
  We propose a novel framework for unsupervised anomaly detection and localization. Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process. Our framework is effective in detecting various real-world defects and achieves a new state-of-the-art in industrial unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-10-09T10:44:58Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)

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.