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1. Continuous Memory Representation for Anomaly Detection NSTL国家科技图书文献中心

Joo Chan Lee |  Taejune Kim... -  《Computer Vision - ECCV 2024,Part LI》 -  European Conference on Computer Vision - 2025, - 438～454 - 共17页

摘要： anomaly detection in an unsupervised manner, where only | , we propose CRAD, a novel anomaly detection method |  tailored for anomaly detection, representing both local |  65.0% of the error for multi-class unified anomaly |  detection. Our project page is available at https://tae
关键词： Anomaly detection |  Continuous memory representation
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2. FARD: Fully Automated Railway Anomaly Detection System NSTL国家科技图书文献中心

Yichen Gao |  Taocun Yang... -  《Social Robotics》 -  International Conference on Social Robotics - 2025, - 293～302 - 共10页

摘要： (Fully Automated Railway Anomaly Detection System |  with reconstruction-based anomaly detection module | Foreign object detection is crucial for |  operations. Current railway foreign object detection |  framework combining traditional object detection pipeline
关键词： Anomaly detection |  Railway safety |  Diffusion models
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3. Feature Consistency Learning for Anomaly Detection NSTL国家科技图书文献中心

Li, Huimin |  Hu, Junlin -  《IEEE Transactions on Instrumentation and Measurement》 - 2025,74(Pt.1) - 5004009.1～5004009.9 - 共9页

摘要：Anomaly detection in industrial images is a |  anomaly detection on publicly available datasets |  anomaly detection tasks. The proposed FCL is capable of |  datasets for industrial image anomaly detection show that |  it is abnormal, but also to locate the anomaly
关键词： Anomaly detection |  feature consistency (FC) |  feature learning |  knowledge distillation
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4. A Survey on Anomaly Detection with Few-Shot Learning NSTL国家科技图书文献中心

Junyang Chen |  Changbo Wang... -  《Cognitive Computing - ICCC 2024》 -  International Conference on Cognitive Computing |  Services Conference Federation - 2025, - 34～50 - 共17页

摘要：The primary objective of anomaly detection is |  challenges when dealing with anomaly detection. To overcome |  construction of models that enhance anomaly detection |  comprehensive investigation of anomaly detection, covering its |  utilization of few-shot learning in anomaly detection across
关键词： Anomaly detection |  Few-shot learning
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5. MoEAD: A Parameter-Efficient Model for Multi-class Anomaly Detection NSTL国家科技图书文献中心

Shiyuan Meng |  Wenchao Meng... -  《Computer Vision - ECCV 2024,Part LXXXV》 -  European Conference on Computer Vision - 2025, - 345～361 - 共17页

摘要： anomaly detection. Despite their appeal, such models |  (SOTA) anomaly detection methods, MoEAD achieves a |  detection approach named MoEAD, which can reduce the model |  size while simultaneously maintaining its detection |  round. This allows MoEAD to capture anomaly semantics
关键词： Multi-class anomaly detection |  Unsupervised anomaly detection |  Industrial scene
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6. FADngs: Federated Learning for Anomaly Detection NSTL国家科技图书文献中心

Boyu Dong |  Dong Chen... -  《IEEE transactions on neural networks and learning systems》 - 2025,36(2) - 2578～2592 - 共15页

摘要： anomaly detection algorithms cannot be directly applied |  representations specific for anomaly detection based on the |  state-of-the-art federated anomaly detection methods |  classification task, overlooking those scenarios where anomaly |  detection may also require privacy-preserving. Traditional
关键词： Anomaly detection |  Density functional theory |  Noise measurement |  Data models |  Self-supervised learning |  Adaptation models |  Training
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7. Learning Diffusion Models for Multi-view Anomaly Detection NSTL国家科技图书文献中心

Chieh Liu |  Yu-Min Chu... -  《Computer Vision - ECCV 2024,Part XXXIII》 -  European Conference on Computer Vision - 2025, - 328～345 - 共18页

摘要： anomaly detection (AD) where multiple instances of the |  anomaly detection. To tackle this problem, our approach |  memory banks of diffusion-based features for anomaly |  detection inference. To demonstrate the efficacy of our | We are exploring an emerging formulation in
关键词： Anomaly detection |  Diffusion model |  ControlNet
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8. GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features NSTL国家科技图书文献中心

Luc P. J. Strater |  Mohammadreza Salehi... -  《Computer Vision - ECCV 2024,Part XXXVII》 -  European Conference on Computer Vision - 2025, - 448～465 - 共18页

摘要：In the domain of anomaly detection, methods | , we present GeneralAD, an anomaly detection |  novel self-supervised anomaly generation module that |  generate interpretable anomaly maps. We extensively |  remaining for both localization and detection tasks. Code
关键词： Anomaly detection |  Self-Supervised learning |  Anomaly localization
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9. Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection NSTL国家科技图书文献中心

Yuanpeng Tu |  Boshen Zhang... -  《Computer Vision - ECCV 2024,Part II》 -  European Conference on Computer Vision - 2025, - 75～91 - 共17页

摘要：Industrial anomaly detection is generally | , numerous 2D anomaly detection methods have been proposed | -oriented representation toward anomaly detection. Both | , in this work, we focus on multimodal anomaly |  detection. Specifically, we investigate early multi-modal
关键词： Self-supervision |  Anomaly detection |  Multi-modality
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10. Anomaly Detection Within Mission-Critical Call Processing NSTL国家科技图书文献中心

Sean Doris |  Iosif Salem... -  《Stabilization, Safety, and Security of Distributed Systems》 -  International Symposium on Stabilization, Safety, and Security of Distributed Systems - 2025, - 322～337 - 共16页

摘要： availability (through anomaly detection) for client-server |  learning to perform anomaly detection of a single |  is possible to generate accurate anomaly detection | With increasingly larger and more complex |  telecommunication networks, there is a need for improved
关键词： Anomaly detection |  Call processing system |  Mission-critical systems |  Virtualized environments |  Machine learning |  Random forest
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