Papers Anomaly Classification
“Anomaly Classification” 태그가 달린 논문 103편 · 필터 해제
CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Medical image anomaly detection remains challenging because networks pretrained on natural images often exhibit limited adaptability to medical images, where abnormal patterns appear as fine-grained local shifts, multi-s…
Anomaly ClassificationAnomaly DetectionPrototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal screening, accurate diagnosis remains ch…
Anomaly ClassificationFew-Shot LearningIndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot understanding across diverse industrial scenarios. However, their performa…
Anomaly ClassificationReinforcement LearningAnomaly DetectionReasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor precise spatial localization of anomalous events. While Vision-Lang…
Video Anomaly DetectionAnomaly ClassificationDomain GeneralizationBinary ClassificationAIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse control systems, frequently failing to dis…
Anomaly ClassificationTAU-R1: Visual Language Model for Traffic Anomaly Understanding
Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in video understanding. However, progress on …
Anomaly ClassificationEI: Early Intervention for Multimodal Imaging based Disease Recognition
Current methods for multimodal medical imaging based disease recognition face two major challenges. First, the prevailing "fusion after unimodal image embedding" paradigm cannot fully leverage the complementary and corre…
parameter-efficient fine-tuningAnomaly ClassificationGSAT: Geometric Traversability Estimation using Self-supervised Learning with Anomaly Detection for Diverse Terrains
Safe autonomous navigation requires reliable estimation of environmental traversability. Traditional methods have relied on semantic or geometry-based approaches with human-defined thresholds, but these methods often yie…
Self-Supervised LearningAnomaly ClassificationAnomaly DetectionAnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional,…
Anomaly ClassificationReinforcement LearningAnomaly DetectionInterpretable Logical Anomaly Classification via Constraint Decomposition and Instruction Fine-Tuning
Logical anomalies are violations of predefined constraints on object quantity, spatial layout, and compositional relationships in industrial images. While prior work largely treats anomaly detection as a binary decision,…
Anomaly ClassificationAnomaly DetectionMRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data distribution, resulting in high training o…
Anomaly ClassificationAnomaly DetectionAnalyzing VLM-Based Approaches for Anomaly Classification and Segmentation
Vision-Language Models (VLMs), particularly CLIP, have revolutionized anomaly detection by enabling zero-shot and few-shot defect identification without extensive labeled datasets. By learning aligned representations of …
Computational EfficiencyAnomaly ClassificationDomain GeneralizationPrompt EngineeringOne Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
Universal visual anomaly detection (AD) aims to identify anomaly images and segment anomaly regions towards open and dynamic scenarios, following zero- and few-shot paradigms without any dataset-specific fine-tuning. We …
Anomaly ClassificationPrompt EngineeringAnomaly DetectionTime-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Quality
Supporting Go/No-Go decisions prior to launch requires assessing real-time telemetry data against redline limits established during the design qualification phase. Family data from ground testing or previous flights is c…
Anomaly ClassificationChain-of-Anomaly Thoughts with Large Vision-Language Models
Automated video surveillance with Large Vision-Language Models is limited by their inherent bias towards normality, often failing to detect crimes. While Chain-of-Thought reasoning strategies show significant potential f…
Anomaly ClassificationAnomaly DetectionSelf-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging
Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the perfo…
Multi-class ClassificationSelf-Supervised LearningRepresentation LearningAnomaly ClassificationOn the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
Zero-shot anomaly classification and segmentation (AC/AS) aim to detect anomalous samples and regions without any training data, a capability increasingly crucial in industrial inspection and medical imaging. This disser…
Anomaly Classification3D Anomaly DetectionCommunity DetectionModeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an e…
Network Intrusion DetectionAnomaly ClassificationAnomaly DetectionCASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection
Deep learning-based 3D anomaly detection methods have demonstrated significant potential in industrial manufacturing. However, many approaches are specifically designed for anomaly detection tasks, which limits their gen…
Point Cloud ClassificationSelf-Supervised LearningRepresentation LearningAnomaly ClassificationProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios
Existing industrial anomaly detection methods mainly determine whether an anomaly is present. However, real-world applications also require discovering and classifying multiple anomaly types. Since industrial anomalies a…
Anomaly ClassificationContrastive LearningAnomaly Detection