Attention-Guided Perturbation for Unsupervised Image Anomaly Detection

• URL: http://arxiv.org/abs/2408.07490v1
• Date: Wed, 14 Aug 2024 12:12:43 GMT
• Title: Attention-Guided Perturbation for Unsupervised Image Anomaly Detection
• Authors: Tingfeng Huang, Yuxuan Cheng, Jingbo Xia, Rui Yu, Yuxuan Cai, Jinhai Xiang, Xinwei He, Xiang Bai,
• Abstract summary: We present a reconstruction framework named Attention-Guided Pertuation Network (AGPNet) It learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.
• Score: 39.48326211958042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstruction-based methods have significantly advanced modern unsupervised anomaly detection. However, the strong capacity of neural networks often violates the underlying assumptions by reconstructing abnormal samples well. To alleviate this issue, we present a simple yet effective reconstruction framework named Attention-Guided Pertuation Network (AGPNet), which learns to add perturbation noise with an attention mask, for accurate unsupervised anomaly detection. Specifically, it consists of two branches, \ie, a plain reconstruction branch and an auxiliary attention-based perturbation branch. The reconstruction branch is simply a plain reconstruction network that learns to reconstruct normal samples, while the auxiliary branch aims to produce attention masks to guide the noise perturbation process for normal samples from easy to hard. By doing so, we are expecting to synthesize hard yet more informative anomalies for training, which enable the reconstruction branch to learn important inherent normal patterns both comprehensively and efficiently. Extensive experiments are conducted on three popular benchmarks covering MVTec-AD, VisA, and MVTec-3D, and show that our framework obtains leading anomaly detection performance under various setups including few-shot, one-class, and multi-class setups.

Related papers

• Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Industrial Anomaly Detection [4.742650815342744]
  Unsupervised anomaly detection is of great significance for quality inspection in industrial manufacturing. We propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. Our method is highly competitive with or significantly outperforms other state-of-the-arts on four public available datasets and one self-made dataset.
  arXiv  Detail & Related papers  (2024-04-20T05:13:56Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy [1.0358639819750703]
  We propose a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR) EAR features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Our approach achieves a high anomaly detection performance without any change of the neural network structure.
  arXiv  Detail & Related papers  (2023-10-06T04:40:48Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• 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)
• Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction [4.033624665609417]
  We propose MemAAE, a novel unsupervised anomaly detection method for time-series. By jointly training two complementary proxy tasks, reconstruction and prediction, we show that detecting anomalies via multiple tasks obtains superior performance. MemAAE achieves an overall F1 score of 0.90 on four public datasets, significantly outperforming the best baseline by 0.02.
  arXiv  Detail & Related papers  (2021-10-15T18:29:05Z)
• Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection [40.778313918994996]
  Reconstruction-based methods play an important role in unsupervised anomaly detection in images. In this work, we interpret the reconstruction of an image as a divide-and-assemble procedure. We achieve state-of-the-art performance on the challenging MVTec AD dataset.
  arXiv  Detail & Related papers  (2021-07-28T01:14:32Z)

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.