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zero-shot anomaly detection

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Showing 1–35 of 35 papers

TitleStatusHype
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection—0
Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge—0
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection—0
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly DetectionsCode1
MFP-CLIP: Exploring the Efficacy of Multi-Form Prompts for Zero-Shot Industrial Anomaly Detection—0
Bayesian Prompt Flow Learning for Zero-Shot Anomaly DetectionCode2
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPCode3
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness—0
Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models—0
LLM Assisted Anomaly Detection Service for Site Reliability Engineers: Enhancing Cloud Infrastructure Resilience—0
KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration—0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly DetectionCode1
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly DetectionCode1
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection—0
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection FrameworkCode2
FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language ModelCode1
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly DetectionCode3
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationCode2
From Zero to Hero: Cold-Start Anomaly DetectionCode0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality LocalizationCode2
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual NavigationCode1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly DetectionCode1
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection—0
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly DetectionCode2
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data—0
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly LocalizationCode1
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationCode2
Zero-Shot Anomaly Detection via Batch NormalizationCode1
MAEDAY: MAE for few and zero shot AnomalY-DetectionCode1
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