{"total":69,"rows":[{"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":"2405.14325","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","created":"2024-05-23","cited_by_count":93,"area":null},{"id":"2407.09359","title":"A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization","created":"2024-07-12","cited_by_count":74,"area":null},{"id":"2407.10862","title":"R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection","created":"2024-07-15","cited_by_count":45,"area":null},{"id":"2305.16713","title":"ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection","created":"2023-05-26","cited_by_count":30,"area":null},{"id":"2412.12617","title":"PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection","created":"2024-12-17","cited_by_count":30,"area":null},{"id":"2407.12427","title":"GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features","created":"2024-07-17","cited_by_count":29,"area":null},{"id":"2412.04867","title":"MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects","created":"2024-12-06","cited_by_count":26,"area":null},{"id":"2308.06748","title":"Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval","created":"2023-08-13","cited_by_count":22,"area":null},{"id":"2405.19882","title":"PixOOD: Pixel-Level Out-of-Distribution Detection","created":"2024-05-30","cited_by_count":20,"area":null},{"id":"2502.19848","title":"One-for-More: Continual Diffusion Model for Anomaly Detection","created":"2025-02-27","cited_by_count":19,"area":"diffusion"},{"id":"2412.17458","title":"Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection","created":"2024-12-23","cited_by_count":17,"area":null},{"id":"2406.18941","title":"CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation","created":"2024-06-27","cited_by_count":16,"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":"2408.15628","title":"CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection","created":"2024-08-28","cited_by_count":12,"area":null},{"id":"2507.03903","title":"Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection","created":"2025-07-05","cited_by_count":11,"area":null},{"id":"2410.14379","title":"AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2411.16110","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","created":"2024-11-25","cited_by_count":8,"area":null},{"id":"2412.07579","title":"Unlocking the Potential of Reverse Distillation for Anomaly Detection","created":"2024-12-10","cited_by_count":8,"area":"distill"},{"id":"2507.15905","title":"Foundation Models and Transformers for Anomaly Detection: A Survey","created":"2025-07-21","cited_by_count":8,"area":null},{"id":"2510.16865","title":"Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection","created":"2025-10-19","cited_by_count":7,"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":"2504.03306","title":"Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection","created":"2025-04-04","cited_by_count":5,"area":null},{"id":"2505.01476","title":"CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering","created":"2025-05-02","cited_by_count":5,"area":null},{"id":"2502.11307","title":"Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection","created":"2025-02-16","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":"2508.12931","title":"Towards High-Resolution Industrial Image Anomaly Detection","created":"2025-08-18","cited_by_count":3,"area":null},{"id":"2303.16191","title":"Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection","created":"2023-03-28","cited_by_count":2,"area":null},{"id":"2401.02287","title":"Distillation-based fabric anomaly detection","created":"2024-01-04","cited_by_count":2,"area":null},{"id":"2407.03634","title":"SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection","created":"2024-07-04","cited_by_count":2,"area":"mllm"},{"id":"2407.17705","title":"ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement","created":"2024-07-25","cited_by_count":2,"area":"ssm"},{"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":"2508.20492","title":"IAENet: An Importance-Aware Ensemble Model for 3D Point Cloud-Based Anomaly Detection","created":"2025-08-28","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":"2601.01856","title":"GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection","created":"2026-01-05","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":"2603.07577","title":"Integration of deep generative Anomaly Detection algorithm in high-speed industrial line","created":"2026-03-08","cited_by_count":2,"area":null},{"id":"2603.16451","title":"TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection","created":"2026-03-17","cited_by_count":2,"area":null},{"id":"2409.06285","title":"Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2502.21012","title":"FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2508.05461","title":"Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection","created":"2025-08-07","cited_by_count":1,"area":"diffusion"},{"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":"2511.18766","title":"Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment","created":"2025-11-24","cited_by_count":1,"area":"diffusion"},{"id":"2605.28428","title":"Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2607.10544","title":"Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection","created":"2026-07-12","cited_by_count":1,"area":null},{"id":"2312.03804","title":"How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection","created":"2023-12-06","cited_by_count":0,"area":null},{"id":"2407.17909","title":"Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approach","created":"2024-07-25","cited_by_count":0,"area":"distill"},{"id":"2408.05681","title":"SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learning","created":"2024-08-11","cited_by_count":0,"area":null},{"id":"2411.14953","title":"Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing","created":"2024-11-22","cited_by_count":0,"area":null},{"id":"2412.08949","title":"Tuned Reverse Distillation: Enhancing Multimodal Industrial Anomaly Detection with Crossmodal Tuners","created":"2024-12-12","cited_by_count":0,"area":"distill"},{"id":"2412.11148","title":"Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detection","created":"2024-12-15","cited_by_count":0,"area":"distill"},{"id":"2504.18802","title":"Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis","created":"2025-04-26","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":"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":"2508.18007","title":"Isolating to Harness: Cross-Division Distillation for Fully Unsupervised Anomaly Detection","created":"2025-08-25","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":"2511.07233","title":"Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection","created":"2025-11-10","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":"2512.13560","title":"3D Human-Human Interaction Anomaly Detection","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2602.07694","title":"Semantic-Deviation-Anchored Multi-Branch Fusion for Unsupervised Anomaly Detection and Localization in Unstructured Conveyor-Belt Coal Scenes","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2603.07566","title":"GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module","created":"2026-03-08","cited_by_count":0,"area":null},{"id":"2604.05632","title":"SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2605.05372","title":"Two Steps Are All You Need: Efficient 3D Point Cloud Anomaly Detection with Consistency Models","created":"2026-05-06","cited_by_count":0,"area":"diffusion"},{"id":"2606.23126","title":"MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection","created":"2026-06-22","cited_by_count":0,"area":"ssm"},{"id":"2606.28688","title":"LogiCo: A Unified Framework for Logical and Structural Anomaly Detection","created":"2026-06-27","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":"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":"2607.13499","title":"M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2609.33353","title":"Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}