Cover Image

On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Le-Gia, Tai, Jaehyun, Ahn
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
by: Le-Gia, Tai
Published: (2025)

Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models
by: Le-Gia, Tai, et al.
Published: (2026)

Understanding normalization in contrastive representation learning and out-of-distribution detection
by: Le-Gia, Tai, et al.
Published: (2023)

AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
by: Ahn, Sunghyun, et al.
Published: (2025)

Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes
by: Wang, Zuzheng, et al.
Published: (2024)

An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices
by: Zhang, Yuezhou, et al.
Published: (2025)

AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
by: Huang, Dayou, et al.
Published: (2025)

AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification
by: Yuan, Jingyi, et al.
Published: (2025)

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
by: Hou, Yanning, et al.
Published: (2025)

ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
by: Chen, Qiuhui, et al.
Published: (2026)

AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis
by: Lai, Zhangyu, et al.
Published: (2025)

SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection
by: Fu, Chenhao, et al.
Published: (2026)

Wavelet-Enhanced PaDiM for Industrial Anomaly Detection
by: Gardner, Cory, et al.
Published: (2025)

AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
by: Fang, Qingqing, et al.
Published: (2025)

PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025)

AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
by: Zhang, Yi, et al.
Published: (2026)

Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
by: Qu, Zhen, et al.
Published: (2025)

Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
by: Wang, Zihan, et al.
Published: (2025)

Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
by: Zhang, Zhaoxiang, et al.
Published: (2024)

MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
by: Xu, Chaoran, et al.
Published: (2026)

No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection
by: Dai, Zunkai, et al.
Published: (2026)

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
by: Yuan, Jingyi, et al.
Published: (2025)

Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
by: Park, SoYoung, et al.
Published: (2025)

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors
by: Fučka, Matic, et al.
Published: (2026)

Zero-Shot Image Anomaly Detection Using Generative Foundation Models
by: Abdi, Lemar, et al.
Published: (2025)

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
by: Chen, Qiyu, et al.
Published: (2025)

Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection
by: Huang, Chaoqin, et al.
Published: (2023)

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
by: Yang, Yingyuan, et al.
Published: (2026)

Dual Distillation for Few-Shot Anomaly Detection
by: Dong, Le, et al.
Published: (2026)

MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
by: Li, Xurui, et al.
Published: (2024)

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
by: Tian, Long, et al.
Published: (2025)

Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
by: Aqeel, Muhammad, et al.
Published: (2026)

Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)

ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
by: Yao, Xincheng, et al.
Published: (2025)

Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
by: Hou, Yanning, et al.
Published: (2025)

SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection
by: Nanaumi, Tomoyasu, et al.
Published: (2026)

Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection
by: Yao, Haiming, et al.
Published: (2024)

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
by: Li, Yanhui, et al.
Published: (2025)

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
by: Chen, Qiyu, et al.
Published: (2025)

Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection
by: Salehi, Alireza, et al.
Published: (2025)