{"total":23,"rows":[{"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":"2601.05552","title":"One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection","created":"2026-01-09","cited_by_count":4,"area":null},{"id":"2601.20524","title":"AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors","created":"2026-01-28","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":"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":"2509.17615","title":"From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge","created":"2025-09-22","cited_by_count":2,"area":null},{"id":"2603.21511","title":"Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection","created":"2026-03-23","cited_by_count":2,"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":"2603.00055","title":"M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection","created":"2026-02-10","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":"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":"2510.21171","title":"TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2511.17339","title":"ReBaPL: Repulsive Bayesian Prompt Learning","created":"2025-11-21","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":"2605.05850","title":"Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.12069","title":"Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection","created":"2026-05-12","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":"2606.16119","title":"EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.16601","title":"DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models","created":"2026-06-15","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":"2608.11933","title":"Dual Anchors, Do It Better: Hierarchical Group Merging for Zero-Shot Anomaly Detection","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2609.16785","title":"PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}