DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；School of Computer Science, Zhejiang University of Technology（浙江工业大学计算机学院）；School of Communication & Information Engineering, Shanghai University（上海大学通信与信息工程学院）
Hui Zhang, Zheng Wang, Dan Zeng, Zuxuan Wu, Yu-Gang Jiang
Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD
Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework. First, to strengthen the constraints on anomalous da…
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection
School of Computer Science, Chengdu University of Information Science and Technology（成都信息科技大学计算机科学学院）；Purdue University（普渡大学）；University at Albany（阿尔巴尼大学）
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
Yonsei College of Medicine（延世大学医学院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Yongin Severance Hospital（龙仁Severance医院）；Yonsei University College of Medicine（延世大学医学院）
A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection
State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University（西安交通大学制造系统工程国家重点实验室）；International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies, Xi’an Jiaotong University（西安交通大学微纳制造与测量技术国际联合研究实验室）
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution
Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（中国科学院大学人工智能学院）；CASI Vision Technology CO., LTD., Luoyang, China（CASI视觉科技有限公司）；School of Artificial Intelligence and Robotics, Hunan University, Changsha, China（湖南大学人工智能与机器人学院）
In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been …
NSF Center for Cloud and Autonomic Computing（NSF云与自主计算中心）；Department of Computer Science（计算机科学系）；The University of Arizona（亚利桑那大学）；The University of North Texas（北得克萨斯大学）
Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveil…
Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as an unsupervised learning problem that involves detecting outliers. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neura…
STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection
Institute of Visual Computing, Graz University of Technology（格拉茨工业大学视觉计算研究所）；Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz（格拉茨医科大学医学信息学、统计学与文档研究所）
Fraunhofer Institute for Physical Measurement Techniques IPM（弗劳恩霍夫物理测量技术研究所IPM）；University of Freiburg（弗莱堡大学）
Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques, i.e., early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further imp…
Machine Learning Group, TU Berlin（柏林技术大学机器学习组）；BIFOLD, Berlin, Germany（柏林BIFOLD）；Dept. of AI, Korea University, Seoul, Republic of Korea（韩国大学人工智能系）；MPI for Informatics, Saarbrücken, Germany（信息学Max Planck研究所）
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of Glasgow（格拉斯哥大学）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection perform…
Denoising Diffusion Models for Anomaly Localization in Medical Images
School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany（生物医学影像机器学习研究所，海德堡慕尼黑德国）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系，瑞士Allschwil）；School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom（伦敦国王学院生物医学工程与影像科学学院，英国）；Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA（耶鲁大学医学院生物医学信息学与数据科学系，美国新罕布什尔州新 Haven）；Yale Biomedical Imaging Institute, Yale University, New Haven, CT, USA（耶鲁大学生物医学影像研究院，美国新罕布什尔州新 Haven）
Hong Kong Baptist University（香港浸会大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University（深圳大学）；Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels. This enables objects to be detected and the identification of materials that have subtle differences between them. However, the increase in spectral resolution often means that there is a decrease in the number of photons received in each channel, which means that the noise linked to the image formation process is greater. This degradation limits the quality of the extracted information and its potential applications…
In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between d…
Self-Supervised Training with Autoencoders for Visual Anomaly Detection
BASLEARN – TU Berlin/BASF Joint Lab for Machine Learning（BASLEARN——柏林工业大学/巴斯夫机器学习联合实验室）；Technische Universität Berlin（柏林工业大学）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；RIKEN Center for AIP（理化学研究所人工智能科学中心）；Korea University（高丽大学）；Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
Science Foundation Ireland Centre for Research Training in Machine Learning（科学基金会爱尔兰机器学习研究培训中心）；School of Medicine（医学院）；School of Computer Science（计算机科学学院）；University College Dublin（都柏林大学）；Insight Centre for Data Analytics（数据分析研究所）
Generating healthy counterfactuals with denoising diffusion bridge models
Athinoula A. Martinos Center for Biomedical Imaging（阿提诺拉A·马丁诺生物医学成像中心）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；ETH Zurich（苏黎世联邦理工学院）；Computer Science & Artificial Intelligence Lab（计算机科学与人工智能实验室）；Hawkes Institute（哈维斯研究所）；University College London（伦敦大学学院）
SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance
Global Institute of Future Technology（未来技术全球研究所）；Shanghai Jiao Tong University（上海交通大学）；Department of Intelligent Manufacturing（智能制造系）；Contemporary Amperex Technology Co.,Ltd（当代电子技术有限公司）
FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China（上海交通大学未来技术全球研究院）；School of Artificial Intelligence, Shenzhen University, Shenzhen, China（深圳大学人工智能学院）；Department of Intelligent Manufacturing, CATL, Ningde, China（CATL智能制造部门）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（香港城市大学计算机科学系）
STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
Global Institute of Future Technology, Shanghai Jiao Tong University（未来技术全球研究院，上海交通大学）；School of Artificial Intelligence, Shenzhen University（人工智能学院，深圳大学）；Department of Intelligent Manufacturing, CATL（智能制造部门，CATL）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
University of Edinburgh（爱丁堡大学）；NHS Lothian（洛锡安国民保健服务体系）；UK Dementia Research Institute Centre, University of Edinburgh（英国痴呆症研究所爱丁堡中心）；University of Dundee（邓迪大学）
Worcester Polytechnic Institute（伍斯特理工学院）；Colorado State University（科罗拉多州立大学）
In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we show how neural networks can be trained to perform all of these tasks simultaneously. While, deep autoencoders implemented by way of neural networks have demonstrated potential for denoising and anomaly detection, standard autoencoders have the drawback that they require access to clean data for training. However, recent work in Robust Deep Autoencoders (RDAEs) shows how autoencoders can be trained to eliminate outliers…
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
School of Electrical Engineering, Korea University, Seoul 02841, Korea（韩国大学电子工程学院）；Department of Pathology, Korea University Anam Hospital and Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02841, Korea（韩国大学医学院病理学系）；Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea（韩国天主大学医学院圣玛丽医院医院病理学系）
Southeast University（东南大学）；University of Massachusetts Dartmouth（马萨诸塞大学达特茅斯分校）
Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs). Our work is inspired by the fact that with noise injected into…
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Birkbeck College（伯克贝克学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）；St George’s University Hospitals NHS Foundation Trust（圣乔治大学医院国民保健服务基金会信托）；Atkinson Morley Regional Neuroscience Centre（阿特金森·莫利地区神经科学中心）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for anomaly detection in medical imaging. Nonetheless, these models still have some intrinsic weaknesses, such as requiring images to be modelled as 1D sequences, the accumulation of errors during the sampling process, and the significant inference times associated with transformers. Denoising diffusion probabilistic models are a class of non-autoregressive generativ…
Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound
Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China Centre for CIMIM, Manchester University, Manchester, UK School of Artificial Intelligence, Shenzhen University, Shenzhen, China National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China School of Biomedical Engineering；Informatics, Nanjing Medical University, Nanjing, China
