{"total":98,"rows":[{"id":"2310.18961","title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","created":"2023-10-29","cited_by_count":271,"area":null},{"id":"2403.12580","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","created":"2024-03-19","cited_by_count":139,"area":null},{"id":"2407.15795","title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","created":"2024-07-22","cited_by_count":127,"area":null},{"id":"2404.05231","title":"PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection","created":"2024-04-08","cited_by_count":98,"area":null},{"id":"2503.06661","title":"AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP","created":"2025-03-09","cited_by_count":73,"area":null},{"id":"2401.16402","title":"A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect","created":"2024-01-29","cited_by_count":69,"area":null},{"id":"2403.12570","title":"Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images","created":"2024-03-19","cited_by_count":69,"area":null},{"id":"2311.14897","title":"Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network","created":"2023-11-25","cited_by_count":67,"area":null},{"id":"2311.00453","title":"CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection","created":"2023-11-01","cited_by_count":58,"area":null},{"id":"2405.14529","title":"AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2","created":"2024-05-23","cited_by_count":53,"area":null},{"id":"2401.16753","title":"MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images","created":"2024-01-30","cited_by_count":52,"area":null},{"id":"2404.13671","title":"FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization","created":"2024-04-21","cited_by_count":51,"area":null},{"id":"2401.12665","title":"ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation","created":"2024-01-23","cited_by_count":49,"area":null},{"id":"2312.07495","title":"Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection","created":"2023-12-12","cited_by_count":47,"area":null},{"id":"2311.02782","title":"Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead","created":"2023-11-05","cited_by_count":40,"area":null},{"id":"2312.07353","title":"CLIP in Medical Imaging: A Survey","created":"2023-12-12","cited_by_count":40,"area":null},{"id":"2405.11315","title":"MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection","created":"2024-05-18","cited_by_count":35,"area":null},{"id":"2410.00320","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","created":"2024-10-01","cited_by_count":29,"area":null},{"id":"2412.03342","title":"UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection","created":"2024-12-04","cited_by_count":28,"area":null},{"id":"2404.04518","title":"MedIAnomaly: A comparative study of anomaly detection in medical images","created":"2024-04-06","cited_by_count":26,"area":null},{"id":"2410.20047","title":"ResAD: A Simple Framework for Class Generalizable Anomaly Detection","created":"2024-10-26","cited_by_count":25,"area":null},{"id":"2406.02263","title":"M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising","created":"2024-06-04","cited_by_count":24,"area":null},{"id":"2410.10289","title":"Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection","created":"2024-10-14","cited_by_count":22,"area":null},{"id":"2409.13162","title":"Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework","created":"2024-09-20","cited_by_count":15,"area":null},{"id":"2412.17619","title":"Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection","created":"2024-12-23","cited_by_count":15,"area":null},{"id":"2403.20236","title":"Long-Tailed Anomaly Detection with Learnable Class Names","created":"2024-03-29","cited_by_count":14,"area":null},{"id":"2504.06740","title":"MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning","created":"2025-04-09","cited_by_count":12,"area":null},{"id":"2504.14221","title":"Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection","created":"2025-04-19","cited_by_count":12,"area":null},{"id":"2603.07952","title":"VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer","created":"2026-03-09","cited_by_count":11,"area":null},{"id":"2307.10792","title":"Optimizing PatchCore for Few/many-shot Anomaly Detection","created":"2023-07-20","cited_by_count":10,"area":null},{"id":"2308.15939","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","created":"2023-08-30","cited_by_count":10,"area":null},{"id":"2506.23810","title":"MadCLIP: Few-shot Medical Anomaly Detection with CLIP","created":"2025-06-30","cited_by_count":10,"area":null},{"id":"2306.09269","title":"Zero-Shot Anomaly Detection with Pre-trained Segmentation Models","created":"2023-06-15","cited_by_count":8,"area":null},{"id":"2406.07176","title":"RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection","created":"2024-06-11","cited_by_count":8,"area":null},{"id":"2501.10067","title":"FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization","created":"2025-01-17","cited_by_count":8,"area":null},{"id":"2503.13184","title":"Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process","created":"2025-03-17","cited_by_count":8,"area":null},{"id":"2507.15905","title":"Foundation Models and Transformers for Anomaly Detection: A Survey","created":"2025-07-21","cited_by_count":8,"area":null},{"id":"2502.01201","title":"One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection","created":"2025-02-03","cited_by_count":7,"area":null},{"id":"2506.03660","title":"INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning","created":"2025-06-04","cited_by_count":7,"area":null},{"id":"2507.19949","title":"AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation","created":"2025-07-26","cited_by_count":7,"area":null},{"id":"2602.23013","title":"SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling","created":"2026-02-26","cited_by_count":7,"area":null},{"id":"2603.14407","title":"Towards One-for-All Anomaly Detection for Tabular Data","created":"2026-03-15","cited_by_count":6,"area":null},{"id":"2404.12246","title":"Blind Localization and Clustering of Anomalies in Textures","created":"2024-04-18","cited_by_count":5,"area":null},{"id":"2407.21351","title":"Small Object Few-shot Segmentation for Vision-based Industrial Inspection","created":"2024-07-31","cited_by_count":5,"area":null},{"id":"2409.20353","title":"CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset","created":"2024-09-30","cited_by_count":5,"area":null},{"id":"2502.19106","title":"A Survey on Foundation-Model-Based Industrial Defect Detection","created":"2025-02-26","cited_by_count":5,"area":null},{"id":"2406.07333","title":"Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection","created":"2024-06-11","cited_by_count":4,"area":null},{"id":"2506.00956","title":"Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection","created":"2025-06-01","cited_by_count":4,"area":null},{"id":"2602.19206","title":"GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning","created":"2026-02-22","cited_by_count":4,"area":null},{"id":"2504.11055","title":"Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection","created":"2025-04-15","cited_by_count":3,"area":null},{"id":"2504.14919","title":"GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection","created":"2025-04-21","cited_by_count":3,"area":null},{"id":"2510.11028","title":"Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts","created":"2025-10-13","cited_by_count":3,"area":null},{"id":"2511.10047","title":"MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2411.09310","title":"Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?","created":"2024-11-14","cited_by_count":2,"area":null},{"id":"2503.01292","title":"PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2506.23577","title":"StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection","created":"2025-06-30","cited_by_count":2,"area":null},{"id":"2507.13378","title":"A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2509.10282","title":"MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection","created":"2025-09-12","cited_by_count":2,"area":null},{"id":"2509.23741","title":"ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2510.10456","title":"On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection","created":"2025-10-12","cited_by_count":2,"area":null},{"id":"2601.16381","title":"VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection","created":"2026-01-23","cited_by_count":2,"area":null},{"id":"2602.15315","title":"Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models","created":"2026-02-17","cited_by_count":2,"area":null},{"id":"2503.23451","title":"Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection","created":"2025-03-30","cited_by_count":1,"area":null},{"id":"2504.11895","title":"Search is All You Need for Few-shot Anomaly Detection","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2508.00777","title":"Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2508.17827","title":"A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection","created":"2025-08-25","cited_by_count":1,"area":null},{"id":"2509.11058","title":"Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection","created":"2025-09-14","cited_by_count":1,"area":null},{"id":"2509.25856","title":"PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detection","created":"2025-09-30","cited_by_count":1,"area":null},{"id":"2601.17405","title":"HAAF: Hierarchical Adaptation and Alignment of Foundation Models for Few-Shot Pathology Anomaly Detection","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2603.19608","title":"FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement","created":"2026-03-20","cited_by_count":1,"area":null},{"id":"2310.04688","title":"PatchProto Networks for Few-shot Visual Anomaly Classification","created":"2023-10-07","cited_by_count":0,"area":null},{"id":"2406.10617","title":"Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation","created":"2024-06-15","cited_by_count":0,"area":null},{"id":"2407.19849","title":"Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2412.03829","title":"CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP","created":"2024-12-05","cited_by_count":0,"area":null},{"id":"2503.01184","title":"Language-Assisted Feature Transformation for Anomaly Detection","created":"2025-03-03","cited_by_count":0,"area":null},{"id":"2505.17692","title":"ViP$^2$-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detection","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2506.13282","title":"Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2507.11003","title":"Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2508.21795","title":"TMUAD: Enhancing Logical Capabilities in Unified Anomaly Detection Models with a Text Memory Bank","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2509.18354","title":"A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2509.18919","title":"Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2510.15602","title":"Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2510.21171","title":"TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2511.22595","title":"AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2602.03594","title":"TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.07154","title":"Beyond Pooling: Matching for Robust Generalization under Data Heterogeneity","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2604.04632","title":"InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2605.16171","title":"Res$^2$CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.25407","title":"Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2605.28630","title":"EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.16119","title":"EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.16153","title":"A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.18749","title":"Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.25273","title":"CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2606.29428","title":"Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2607.01949","title":"LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2608.00695","title":"FreqAnchorAD: Language-Free Zero-Shot Anomaly Detection via Frequency-Deviation Anchoring","created":"2026-08-01","cited_by_count":0,"area":null},{"id":"2608.11933","title":"Dual Anchors, Do It Better: Hierarchical Group Merging for Zero-Shot Anomaly Detection","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}