Anomaly Classification
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Benchmarks
Most implemented
Towards Total Recall in Industrial Anomaly Detection
Anomaly Detection via Reverse Distillation from One-Class Embedding
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Papers
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 Classification