{"total":15,"rows":[{"id":"2301.11514","title":"Deep Industrial Image Anomaly Detection: A Survey","created":"2023-01-27","cited_by_count":148,"area":null},{"id":"2303.05768","title":"Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection","created":"2023-03-10","cited_by_count":21,"area":null},{"id":"2309.16902","title":"Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation","created":"2023-09-29","cited_by_count":6,"area":null},{"id":"2408.13561","title":"Variational Autoencoder for Anomaly Detection: A Comparative Study","created":"2024-08-24","cited_by_count":6,"area":null},{"id":"2510.17611","title":"Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection","created":"2025-10-20","cited_by_count":6,"area":null},{"id":"2306.03551","title":"Scalable Concept Extraction in Industry 4.0","created":"2023-06-06","cited_by_count":2,"area":null},{"id":"2410.14844","title":"SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2503.04997","title":"ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2511.20169","title":"ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories","created":"2025-11-25","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":"2504.13560","title":"Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2505.03412","title":"CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection","created":"2025-05-06","cited_by_count":0,"area":null},{"id":"2506.16050","title":"Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments","created":"2025-06-19","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":"2607.14760","title":"Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoring","created":"2026-07-16","cited_by_count":0,"area":null}],"next_cursor":null}