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-occlusions and specularities. Furthermore, we achieve large intra-class variation by randomly drawing key parameters of a procedural rendering pipeline, which enables the creation of an arbitrary number of instances with photo-realistic appearance. Likewise, anomalies are injected into the rendering graph and pixel-wise annotations are automatically generated, overcoming human-biases and possible inconsistencies.
We believe this dataset may encourage the exploration of original approaches to solve the anomaly detection task, e.g. by combining color, depth and normal maps, as they are not provided by most of the existing datasets. Indeed, in order to demonstrate how exploiting additional information may actually lead to higher detection performance, we show the results obtained by training a deep convolutional autoencoder to reconstruct different combinations of inputs.
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（海湾大学计算机与信息学院）
SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造学域）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；The Hong Kong University of Science and Technology（香港科技大学）
CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection
State Key Laboratory of Opto-Electronic Information Acquisition；Protection Technology, Anhui University, Hefei, China；School of Artificial Intelligence, Anhui University, Hefei, China；College of Intelligence Science；Technology, National University of Defense Technology, Changsha, China；School of Mathematics \& Physics, Anhui Jianzhu University, Hefei, China
Multimodal Industrial Anomaly Detection via Geometric Prior
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（教育部计算功率网络与信息安全重点实验室，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算功率互联网与服务计算重点实验室，山东省计算机科学基础研究中心）
BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；State Key Lab of IOTSC, Department of CIS, University of Macau（物联网科学国家重点实验室，澳门大学）
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Electronics Telecommunications Research Institute（电子电信研究院）；Univ. of Science Technology（科学技术大学）；ETH Zurich（苏黎世联邦理工学院）；Information Technology Univ（信息技术大学）；Mohamed bin Zayed Univ. of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
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））
StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised 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（广东中医药实验室）
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Academy for Engineering & Technology, Fudan University（复旦大学工程技术学院）；Shanghai Engineering Research Center of AI & Robotics（上海市人工智能与机器人工程研究中心）；Engineering Research Center of AI & Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Key Laboratory of Computing Power Network（计算能力网络重点实验室）；Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（信息安全，教育部，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computing Power Internet（山东省计算能力互联网重点实验室）；Service Computing, Shandong Fundamental Research Center for Computer Science（服务计算，山东省计算机基础研究中心）；Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造方向）；The Hong Kong University of Science and Technology（香港科技大学）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection
CNRS（法国国家科学研究中心）；LS2N（南特数字科学实验室）；Movement - Interactions - Performance（运动-互动-表现研究所）；IP UR 4334 UFR STAPS（体育与运动科学学院）；CHU Nantes（南特大学医院）；INSERM（法国国家健康与医学研究院）；l’institut du thorax（胸部研究所）；Institut Universitaire de France (IUF)（法国大学研究院）
