Papers Anomaly Localization
“Anomaly Localization” 태그가 달린 논문 101편 · 필터 해제
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significant challenges, including high computatio…
Anomaly DetectionAnomaly LocalizationHybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles
We propose a hybrid meta-learning framework for forecasting and anomaly detection in nonlinear dynamical systems characterized by nonstationary and stochastic behavior. The approach integrates a physics-inspired simulato…
Anomaly DetectionAnomaly ForecastingAnomaly LocalizationMeta-Learning+1PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to a…
Adversarial RobustnessAnomaly DetectionAnomaly LocalizationTrack Any Anomalous Object: A Granular Video Anomaly Detection Pipeline
Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Although existing methods have primarily focused on detecti…
Anomaly DetectionAnomaly LocalizationAutonomous DrivingVideo Anomaly DetectionBridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due t…
3D Anomaly DetectionAnomaly DetectionAnomaly LocalizationNOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Out-of-distribution detection identifies whether an input stems from an unseen distribution, while open-wo…
Anomaly LocalizationBenchmarkingDiagnosticImage Captioning+1Vision Foundation Model Embedding-Based Semantic Anomaly Detection
Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonomous systems. This work explores semantic…
Anomaly DetectionAnomaly LocalizationInstance SegmentationSemantic SegmentationChain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization
Traditional temporal action localization (TAL) methods rely on large amounts of detailed annotated data, whereas few-shot TAL reduces this dependence by using only a few training samples to identify unseen action categor…
Action LocalizationAnomaly DetectionAnomaly LocalizationFew-Shot Learning+5Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections
Anomaly Detection (AD) involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection. Traditional AD methods typically require the av…
Anomaly DetectionAnomaly LocalizationDefect DetectionPrompt Learning+2Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
Recent advances in convolutional neural networks (CNNs) and transformer-based methods have improved anomaly detection and localization, but challenges persist in precisely localizing small anomalies. While CNNs face limi…
Anomaly DetectionAnomaly LocalizationMambaMulti-class Anomaly Detection+1AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawarenes…
Anomaly DetectionAnomaly LocalizationLesion Detectionzero-shot anomaly detectionOFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-Adjustment
Contrastive Language-Image Pre-Training (CLIP) has enabled zero-shot classification in radiology, reducing reliance on manual annotations. However, conventional contrastive learning struggles with normal case detection d…
Anomaly LocalizationClassificationClusteringContrastive Learning+3MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy for time series
Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and…
Anomaly DetectionAnomaly LocalizationMambaTime Series+1Transformer-based Multivariate Time Series Anomaly Localization
With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequ…
Anomaly DetectionAnomaly LocalizationTime SeriesTrack Any Anomalous Object:A Granular Video Anomaly Detection Pipeline
Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Albeit existing methods have primarily focused on detec…
Anomaly DetectionAnomaly LocalizationAutonomous DrivingImage Segmentation+2Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augmen…
Anomaly ClassificationAnomaly DetectionAnomaly LocalizationDiversity+1Towards Zero-shot 3D Anomaly Localization
3D anomaly detection and localization is of great significance for industrial inspection. Prior 3D anomaly detection and localization methods focus on the setting that the testing data share the same category as the trai…
3D Anomaly DetectionAnomaly DetectionAnomaly LocalizationContrastive Learning+1Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection
Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cro…
Anomaly DetectionAnomaly LocalizationContrastive LearningPaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification
Convolutional neural networks (CNNs) are extremely popular and effective for image classification tasks but tend to be overly confident in their predictions. Various works have sought to quantify uncertainty associated w…
Anomaly Localizationimage-classificationImage ClassificationUncertainty QuantificationDenoising Diffusion Models for Anomaly Localization in Medical Images
This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and th…
Anomaly LocalizationDenoisingImage Reconstruction