Papers Anomaly Classification
“Anomaly Classification” 태그가 달린 논문 103편 · 필터 해제
Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach
Detecting anomalies in multivariate time series is essential for monitoring complex industrial systems, where high dimensionality, limited labeled data, and subtle dependencies between sensors cause significant challenge…
Time Series Anomaly DetectionAnomaly ClassificationReinforcement LearningActive LearningMuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples
Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: n…
Anomaly ClassificationTRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection
Video anomalies often depend on contextual information available and temporal evolution. Non-anomalous action in one context can be anomalous in some other context. Most anomaly detectors, however, do not notice this typ…
Video Anomaly DetectionAnomaly ClassificationGRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques
This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors that integrates statistical analysis and deep learning to ensure the reliability of sensor data. The proposed approach combi…
Anomaly ClassificationOutlier DetectionAnomaly DetectionHuman-Centric Anomaly Detection in Surveillance Videos Using YOLO-World and Spatio-Temporal Deep Learning
Anomaly detection in surveillance videos remains a challenging task due to the diversity of abnormal events, class imbalance, and scene-dependent visual clutter. To address these issues, we propose a robust deep learning…
Anomaly Detection In Surveillance VideosAnomaly ClassificationOn the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
Zero-shot image anomaly classification (AC) and segmentation (AS) are vital for industrial quality control, detecting defects without prior training data. Existing representation-based methods compare patch features with…
Anomaly ClassificationCommunity DetectionAnomaly DetectionBenchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CT
Non-contrast computed tomography (NCCT) is essential for rapid stroke diagnosis but is limited by low image contrast and signal to noise ratio. We address this challenge by leveraging DINOv3, a state-of-the-art self-supe…
Anomaly ClassificationDinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of expert labeling. Emerging vision founda…
Anomaly ClassificationContrastive LearningAnomaly DetectionA layered architecture for log analysis in complex IT systems
In the evolving IT landscape, stability and reliability of systems are essential, yet their growing complexity challenges DevOps teams in implementation and maintenance. Log analysis, a core element of AIOps, provides cr…
Anomaly ClassificationAnomaly DetectionStructured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans
With the increasing number of CT scan examinations, there is a need for automated methods such as organ segmentation, anomaly detection and report generation to assist radiologists in managing their increasing workload. …
Multi-Label ClassificationAnomaly ClassificationAnomaly DetectionGraph LearningAnomalous Samples for Few-Shot Anomaly Detection
Several anomaly detection and classification methods rely on large amounts of non-anomalous or "normal" samples under the assump- tion that anomalous data is typically harder to acquire. This hypothesis becomes questiona…
Anomaly ClassificationAnomaly DetectionBridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection
With the advent of vision-language models (e.g., CLIP) in zero- and few-shot settings, CLIP has been widely applied to zero-shot anomaly detection (ZSAD) in recent research, where the rare classes are essential and expec…
Anomaly ClassificationAnomaly Detectioncross-modal alignmentzero-shot anomaly detectionA Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma
Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant proportion of cancer cases. Early detection d…
Anomaly ClassificationDiagnosticSuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect
In this technical report, we present our solution to the CVPR 2025 Visual Anomaly and Novelty Detection (VAND) 3.0 Workshop Challenge Track 1: Adapt & Detect: Robust Anomaly Detection in Real-World Applications. In real-…
Anomaly ClassificationAnomaly DetectionNovelty DetectionFew-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or ex…
Anomaly ClassificationAnomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionDetect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models
Recent advances in visual industrial anomaly detection have demonstrated exceptional performance in identifying and segmenting anomalous regions while maintaining fast inference speeds. However, anomaly classification-di…
Anomaly ClassificationAnomaly DetectionAnomaly Severity Classification (Anomaly vs. Defect)Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiolog…
Anomaly ClassificationComputed Tomography (CT)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Video Anomaly Detection with Structured Keywords
This paper focuses on detecting anomalies in surveillance video using keywords by leveraging foundational models' feature representation generalization capabilities. We present a novel, lightweight pipeline for anomaly c…
Anomaly ClassificationAnomaly DetectionVideo Anomaly DetectionDeep Subspace Learning for Surface Anomaly Classification Based on 3D Point Cloud Data
Surface anomaly classification is critical for manufacturing system fault diagnosis and quality control. However, the following challenges always hinder accurate anomaly classification in practice: (i) Anomaly patterns e…
Anomaly ClassificationClassificationFault DiagnosisAnomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication
Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification a…
Anomaly ClassificationAnomaly DetectionAutonomous Driving