{"total":20,"rows":[{"id":"2510.00495","title":"Normal-Abnormal Guided Generalist Anomaly Detection","created":"2025-10-01","cited_by_count":10,"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":"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":"2510.26464","title":"Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection","created":"2025-10-30","cited_by_count":4,"area":null},{"id":"2603.25159","title":"A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection","created":"2026-03-26","cited_by_count":3,"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":"2509.23741","title":"ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2512.01672","title":"ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models","created":"2025-12-01","cited_by_count":2,"area":null},{"id":"2601.22763","title":"Is Task-Specific Training Necessary for Anomaly Detection?","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2511.05245","title":"ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining","created":"2025-11-07","cited_by_count":1,"area":null},{"id":"2605.26857","title":"Generalist Graph Anomaly Detection via Prototype-Based Distillation","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2601.19833","title":"A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection","created":"2026-01-27","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":"2605.23231","title":"Beyond Normal References: Discriminative Few-Shot Anomaly Detection","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.26193","title":"Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.14475","title":"Value-order Decomposition for Generalist Anomaly Detection","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.11170","title":"TC-MAF: Train-Calibrated Bounded Multi-Evidence Fusion for Multimodal Industrial Anomaly Detection","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2609.35059","title":"Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modeling","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.07016","title":"Anchor and Adapt: Asymmetric Prompt Adaptation for Few-Shot Industrial Anomaly Detection","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}