We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localization). We analyze its original loss function and propose a substitute that better resembles its predecessor, the Hypersphere Classifier (HSC). Both are compared on the MVTec Anomaly Detection Dataset (MVTec-AD) -- training images are flawless objects/textures and the goal is to segment unseen defects -- showing that consistent improvement is achieved by better designing the pixel-wise supervision.
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
Faculty of Computing, Harbin Institute of Technology, Harbin, China University of New South Wales, Sydney, Australia School of Biomedical Engineering, Tsinghua University, Beijing, China School of Artificial Intelligence；Robotics, Hunan University, Changsha, China Big data institute, Central South University, Sydney, Australia DZ-Matrix, Beijing, China Chongqing Research Institute of HIT, Chongqing, China
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）
ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）；School of Information Science and Technology, Beijing Forestry University（北京林业大学信息科学与技术学院）
Cranberry-Lemon University（蔓越莓柠檬大学）；University of the Witwatersrand（金山大学）
Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pretrained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pretrained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods. In order to obtain further performance gain…
For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learn…
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 …
Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples. Recently, large gains were made on this task for the domain of natural images using self-supervised contrastive feature learning as a first step followed by kNN or traditional one-class classifiers for feature scoring. Learned representations that are non-uniformly distributed on the unit hypersphere have been shown to be beneficial for this task. We go a step further and investigate how the \emph {geometrical compa…
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
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）
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；AI for Good (AIGO) Research Unit, Istituto Italiano di Tecnologia（意大利技术研究院人工智能为善研究单元）
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
University of Science and Technology of China（中国科学技术大学）；Northeastern University（东北大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CASI Vision Technology CO., LTD.（卡西视觉科技有限公司）；Binzhou Institute of Technology, Weigiao-UCAS Science and Technology Park（威高-国科大科技园滨州理工学院）
TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection
Zhejiang University（浙江大学）；Tencent YouTu Lab（腾讯YouTu实验室）；Singapore Management University（新加坡管理学院）；Qilu University of Technology (Shandong Academy of Sciences)（青岛科技大学（山东科学院））
ResAD: A Simple Framework for Class Generalizable Anomaly Detection
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；China Pacific Insurance (Group) Co., Ltd.（中国太平洋保险（集团）股份有限公司）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院、教育部人工智能重点实验室）
CCAD: Compressed Global Feature Conditioned Anomaly Detection
Columbia University（哥伦比亚大学）；Ping An Property & Casualty Insurance Company（平安财产保险股份有限公司）；University of Michigan（密歇根大学）；University of California at Berkeley（加州大学伯克利分校）；Stevens Institute of Technology（史蒂文斯理工学院）；VisionX LLC.（VisionX有限责任公司）
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
College of Information and Engineering, Capital Normal University（首都师范大学信息工程学院）；Peking University Third Hospital（北京大学第三医院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）
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）
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（安全、可靠性与信任跨学科研究中心（SnT），卢森堡大学）；Cristal Laboratory, National School of Computer Sciences, University of Manouba（晶体实验室，国家计算机科学学校，曼努巴大学）
Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications
Institute of Science Tokyo（东京科学研究所）；Institute of Integrated Research（综合研究院）；BioMedical Artificial Intelligence (BMAI) Research Unit（生物医学人工智能（BMAI）研究单元）
