ProCon: Projection-Consistency Memory for Training-Free 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（温州医科大学附属第五医院）；Chinese Medicine Guangdong Laboratory（广东中医药大学实验室）
Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory methods still use the memory bank as a nearest-neighbor lookup table: a test patch is treated as normal if it has one nearby normal anchor. This hard retrieval view is vulnerable to false-normal matches and does not test whether the patch is consistently supported by a local normal neighborhood. We propose ProCon, a training-free framework that turns memory retrieval into decoder-free reconstruction. ProCon softly projects each test patch onto nearby normal memory vectors and uses the projection residual as anomaly evidence. To stabilize this residual, it constructs seed-perturbed layer-wise memories, aggregates bank residuals by a median, and fuses depth-specific residual maps by layer consensus. ProCon requires no decoder training, backbone fine-tuning, learned fusion weights, or pseudo-anomaly supervision. Across MVTec-AD, VisA, and Real-IAD under the single-category evaluation protocol, ProCon achieves strong image- and pixel-level performance under seven standard metrics, including image AUROC scores of 99.8%, 99.2%, and 93.2%, respectively. Ablations show that the gains come from replacing hard retrieval with soft normal projection and stabilizing the residuals through memory and depth consensus. The code is available at https://github.com/jw-chae/Procon
Shenzhen Institute of Advanced Study, UESTC（电子科技大学深圳高等研究院）
Anomaly detection aims to separate anomalies from normal samples, and the pretrained network is promising for anomaly detection. However, adapting the pretrained features would be confronted with the risk of pattern collapse when finetuning on one-class training data. In this paper, we propose an anomaly detection framework called constrained adaptive projection with pretrained features (CAP). Combined with pretrained features, a simple linear projection head applied on a specific input and its k most similar pretrained normal representations is designed for feature adaptation, and a reformed …
University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are difficult to be synthesized realistically due to their similarity to anomaly-free regions. We propose an architecture based on quantized feature space representation with dual decoders, DSR, that avoids the image-level anomaly synthesis requirement. Without making any assumptions about the visual properties of anomalies, DSR generates the anomalies at t…
Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection
School of Engineering and Built Environment, Griffith University（格里菲斯大学工程与环境学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）
Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. However, existing methods rely on supervised learning, imposing an additional burden to doctors for collecting disease data or annotating spatial labels for network training, consequently hindering their implementation. We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction for unsupervised anomaly dete…
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）
Anomalies are by definition rare, thus labeled examples are very limited or nonexistent, and likely do not cover unforeseen scenarios. Unsupervised learning methods that don't necessarily encounter anomalies in training would be immensely useful. Generative vision models can be useful in this regard but do not sufficiently represent normal and abnormal data distributions. To this end, we propose constant curvature manifolds for embedding data distributions in unsupervised visual anomaly detection. Through theoretical and empirical explorations of manifold shapes, we develop a novel hyperspheri…
NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Sony Computer Science Laboratories (Sony CSL)（索尼计算机科学实验室）；The University of Tokyo（东京大学）；National Taiwan Normal University (NTNU)（国立台湾师范大学（NTNU））
GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
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）
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection
School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC)（电子科技大学信息与通信工程学院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Laboratoire des Signaux et Systemes, Universite Paris-Saclay, CNRS, CentraleSupelec（巴黎萨克雷大学信号与系统实验室、法国国家科学研究中心、中央理工学院）
Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its autoencoder reconstruction. In practice however, local defects added to a normal image can deteriorate the whole reconstruction, making this segmentation challenging. To tackle the issue, we propose in this paper a new approach for projecting anomalous data on a autoencoder-lear…
One-for-More: Continual Diffusion Model for Anomaly Detection
East China Normal University（东华师范大学）；Shanghai Innovation Institute（上海创新研究院）；Xiamen University（厦门大学）；Shanghai Jiao Tong University（上海交通大学）；Tencent YouTu Lab（腾讯YouTu实验室）；Tencent WeChatPay Lab33（腾讯微信支付实验室）；CATL（宁德时代）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China（教育部多媒体可信感知与高效计算重点实验室）
本文针对扩散模型在持续异常检测中的保真幻觉与灾难性遗忘问题，提出基于梯度投影的持续扩散模型，并通过迭代奇异值分解降低内存开销，同时引入异常掩码网络增强条件机制，在 MVTec 和 VisA 上取得领先性能。
Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise) and reconstruction loss (pixel-wise). It is natural and straightforward to use the later as the predictor. However, usually local anomaly added to a normal image can deteriorate the whole reconstructed image, causing segmentation us…
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to problems such as anomaly detection and adversarial defense. A recurring theme in many of these applications is the notion of projecting an image observation onto the manifold that is inferred by the generator. In this context, Projected Gradient Descent (PGD) has been the most …
NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning
Guangzhou Institute of Industrial Intelligence（广州工业智能研究院）；School of Architecture & Civil Engineering（建筑与土木工程学院）；School of Information Science and Engineering（信息科学与工程学院）；College of Environmental Science and Engineering（环境科学与工程学院）
Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Cambridge University Hospitals（剑桥大学医院）；Mitera Hospital（米特拉医院）；Radboud University Medical Center（拉德堡德大学医学中心）；University Hospital Aachen（亚琛大学医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ribera Hospital（里贝拉医院）；Duke Breast Cancer MRI（杜克乳腺癌磁共振成像项目）
Medical Faculty Mannheim of Heidelberg University（海德堡大学曼海姆医学院）
Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning (DL) has shown promise in modeling complex spatial data for brain anomaly detection. However, DL models have major deficiencies: they need large amounts of high-quality training data, are difficult to design and train and are sensitive to subtle changes in scanning protocols and hardware. Here, we show that also simple statistical methods such as voxel-wise (baseline and covariance) models and a linear projection method using spatial pattern…
