Image anomaly detection and localization perform not only image-level anomaly classification but also locate pixel-level anomaly regions. Recently, it has received much research attention due to its wide application in various fields. This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization. First, the patch features of normal images are extracted by a deep network pre-trained on nature images. Then, the prototypes of the normal patch features are learned by non-parametric clustering. Finally, we construct an image anomaly localization network (ProtoAD) by appending the feature extraction network with $L2$ feature normalization, a $1\times1$ convolutional layer, a channel max-pooling, and a subtraction operation. We use the prototypes as the kernels of the $1\times1$ convolutional layer; therefore, our neural network does not need a training phase and can conduct anomaly detection and localization in an end-to-end manner. Extensive experiments on two challenging industrial anomaly detection datasets, MVTec AD and BTAD, demonstrate that ProtoAD achieves competitive performance compared to the state-of-the-art methods with a higher inference speed. The source code is available at: https://github.com/98chao/ProtoAD.
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
School of Mathematics, Shandong University（山东大学数学学院）；Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院介入医学与微创肿瘤科）；Department of Neurosurgery, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院神经外科）
Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；School of Information and Software Engineering, East China Jiaotong University（华东交通大学信息工程学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Department of Artificial Intelligence, FPT University（FPT大学人工智能系）；Department of IT, FPT University（FPT大学信息技术系）；Department of Computing Fundamental, FPT University（FPT大学计算基础系）
Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors. The video inputs with large reconstruction errors are regarded as anomalies at the test time. However, these methods sometimes reconstruct abnormal inputs well because of the powerful generalization ability of deep autoencoder. To address this problem, we present a novel approach for anomaly detection, which utilizes discriminative prototypes of normal data to reconstruct video frames. In this way, the model will favor the reconstruction of no…
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
Cooperative Medianet Innovation Center（未来媒体网络协同创新中心）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical diagnosis and industrial defect detection, anomalies only present in a fraction of the images. To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization. SSM not only enhances the training of the inpaintin…
Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）；Northwestern Polytechnical University（西北工业大学）
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space. (2) The fixed number of prototypes from subjective assumptions ignores the data feature differences and diversity. To overcome these drawbacks, we introduce DLAN-AC, a Dynamic Local Aggregation Network with Adaptive Clusterer, for anomaly detection. First, The proposed DLAN can automatically learn and aggregate high-level features from the AE to obtain more representative prototypes, while freeing up extra memory space.…
We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-wor…
Imperial College London（帝国理工学院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD …
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representation-based approaches extract normal image features with a deep convolutional neural network and characterize the corresponding distribution through non-parametric distribution estimation methods. The anomaly score is calculated by measuring the distance between the feature of the test image and the estimated distribution. However, current methods can not effectively map image features to a tractable base distributio…
Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
School of Information Engineering（信息工程学院）；Suqian University（宿迁大学）；School of Electronic & Information Engineering（电子与信息工程学院）；Nanjing University of Information Science and Technology（南京信息科学技术大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Institute for Advanced Studies（高级研究机构）；Universiti Malaya（马来亚大学）
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CASI Vision Technology CO., LTD.（CASI视觉技术有限公司）
Malek Ashtar University of Technology（马利克·阿什塔尔理工大学）；Iran University of Science and Technology（伊朗科技大学）；Auckland University of Technology（奥克兰理工大学）
In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto- encoder. Experimen…
University of Science Technology of China（中国科学技术大学）；Meka Technology Co Ltd（美卡科技有限公司）
We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieved by learning a data distribution of normal samples and detecting anomalies as regions in the image which deviate from this distribution. Most current state-of-the-art methods use latent variable generative models operating directly on the images. However, generative models have been shown to mostly capture low-level features, s.a. pixel-intensities, instead of rich semantic features, which also applies to their repr…
We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providing depth and normal maps in an industrial conveyor scenario. We make available anomaly-free samples for model training and validation, while anomalous instances with precise ground-truth annotations are provided only in the test set. The dataset comprises ten classes of candies, each showing different challenges, such as complex textures, self-occlu…
We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not c…
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage 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. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anoma…
CY Cergy Paris Université（CY塞尔吉-巴黎大学）；ENSEA（国立高等电子及应用学院）；CNRS（法国国家科学研究中心）；Idemia Identity & Security（艾德米尔身份与安全公司）
Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings. However current AD models still struggle on edge-case normal samples and are often unable to keep high performance over different scales of anomalies. Moreover, there currently does not exist a unified framework efficiently covering both one-class and unbalanced learnings. In the light of these limitations, we introduce a new two-stage anomaly detector which memorizes during training multi-scale normal prototypes to compute an anomaly deviation score. First, we simultaneously lea…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Information Technology University（信息技术大学）
In modern intelligent video surveillance systems, automatic anomaly detection through computer vision analytics plays a pivotal role which not only significantly increases monitoring efficiency but also reduces the burden on live monitoring. Anomalies in videos are broadly defined as events or activities that are unusual and signify irregular behavior. The goal of anomaly detection is to temporally or spatially localize the anomaly events in video sequences. Temporal localization (i.e. indicating the start and end frames of the anomaly event in a video) is referred to as frame-level detection.…
Nanjing University of Science and Technology（南京理工大学）
Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples. Unfortunately, the existing memory module is not fully applicable to the anomaly detection task, and the reconstruction error of the anomaly samples remains small. Towards this end, this work proposes a new unsupervised visual anomaly detection method to jointly learn effective normal features and eliminate unfavorable reconstruction errors. Specifically, a novel Partition Memory Bank (PMB) module is proposed to e…
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；School of Mechanical Science & Engineering, Huazhong University of Science & Technology（华中科技大学机械科学与工程学院）
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Di Hub；Iran University of Science and Technology（伊朗科技大学）；Auckland University of Technology（奥克兰理工大学）
The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representat…
GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection
Tsinghua University Shenzhen International Graduate School（清华大学深圳国际研究生院）；Ratel Soft；Affiliated Fifth Hospital, Wenzhou Medical University（温州医学院附属第五医院）；Chinese Medicine Guangdong Laboratory（广东中医药实验室）
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；School of Artifcial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Wuhan AI Research（武汉AI研究所）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University（广东省知识产权与大数据重点实验室，广东工业大学）
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have access to only anomaly-free training data and want to detect and identify anomalies of an arbitrary nature on test data. We employ the density estimates from the energy-based model (EBM) as normalcy scores that can be used to discriminate normal images from anomalous ones. Further, we back-propagate the gradients of the energy score with respect to the image in order to generate a gradient map that provides pixel-level sp…
Gauss Labs Inc.（高斯实验室公司）；Seoul National University（首尔国立大学）
Because anomalous samples cannot be used for training, many anomaly detection and localization methods use pre-trained networks and non-parametric modeling to estimate encoded feature distribution. However, these methods neglect the impact of position and neighborhood information on the distribution of normal features. To overcome this, we propose a new algorithm, \textbf{PNI}, which estimates the normal distribution using conditional probability given neighborhood features, modeled with a multi-layer perceptron network. Moreover, position information is utilized by creating a histogram of rep…
University of Udine（乌迪内大学）；beanTech srl（beanTech有限责任公司）
We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding. The use of transformer networks helps to preserve the spatial information of the embedded patches, which are later processed by a Gaussian mixture density network to localize the anomalous areas. In addition, we also publish BTAD, a real-world industrial anomaly dataset. Our results are compared with other state-of-the-art algorithms using publicly available datasets like MNIST and MVTec.
Image anomaly detection problems aim to determine whether an image is abnormal, and to detect anomalous areas. These methods are actively used in various fields such as manufacturing, medical care, and intelligent information. Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. Therefore, current image anomaly detection methods have com…
University of Ljubljana（卢布尔雅那大学）；Faculty of Computer Information Science（计算机信息科学学院）
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies. These methods are trained only on anomaly-free images, and often require hand-crafted post-processing steps to localize the anomalies, which prohibits optimizing the feature extraction for maximal detection capability. In addition to reconstructive approach, we cast surface anomaly detection primarily as a discriminative problem and propose a …
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密测量技术与仪器国家重点实验室，精密仪器系）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备技术国家重点实验室）；Hunan University School of Robotics（湖南大学机器人学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Panasonic AI Lab（松下人工智能实验室）；Panasonic Technology Division（松下技术事业部）
Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a disc…
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）
Detection of object anomalies is crucial in industrial processes, but unsupervised anomaly detection and localization is particularly important due to the difficulty of obtaining a large number of defective samples and the unpredictable types of anomalies in real life. Among the existing unsupervised anomaly detection and localization methods, the NF-based scheme has achieved better results. However, the two subnets (complex functions) $s_{i}(u_{i})$ and $t_{i}(u_{i})$ in NF are usually multilayer perceptrons, which need to squeeze the input visual features from 2D flattening to 1D, destroying…
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jiangxi Normal University（江西师范大学）；Southern University of Science and Technology（南方科技大学）；Swansea University（斯旺西大学）；Zhejiang University（浙江大学）；Anhui University（安徽大学）；Northwestern Polytechnical University（西北工业大学）
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所基础模型研究中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Objecteye Inc., Beijing, China（Objecteye公司）；Central South University, Hunan, China（中南大学）
SenseTime Research（商汤科技研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of California, Los Angeles（加州大学洛杉矶分校）
The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a high-resolution image especially for industrial applications. Towards this end, 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. The coarse alignment stage standardizes the pixel-wise position of objects in both image and …
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization
organization= School of Software Engineering,Xi'an Jiaotong University , addressline= 28 Xianning West Road , city= Xi'an , postcode= 710049 , state= Shaanxi , country= China
