{"total":51,"rows":[{"id":"2502.07601","title":"Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models","created":"2025-02-11","cited_by_count":54,"area":"lr"},{"id":"2407.21794","title":"Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey","created":"2024-07-31","cited_by_count":36,"area":"mllm"},{"id":"2503.10080","title":"Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection","created":"2025-03-13","cited_by_count":31,"area":"mllm"},{"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":"2409.01980","title":"Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey","created":"2024-09-03","cited_by_count":27,"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":"2503.13195","title":"Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey","created":"2025-03-17","cited_by_count":17,"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":"2409.20146","title":"VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection","created":"2024-09-30","cited_by_count":13,"area":"mllm"},{"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":"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":"2507.15905","title":"Foundation Models and Transformers for Anomaly Detection: A Survey","created":"2025-07-21","cited_by_count":8,"area":null},{"id":"2508.06203","title":"AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection","created":"2025-08-08","cited_by_count":8,"area":"moe"},{"id":"2602.00522","title":"MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval","created":"2026-01-31","cited_by_count":6,"area":"mllm"},{"id":"2502.19106","title":"A Survey on Foundation-Model-Based Industrial Defect Detection","created":"2025-02-26","cited_by_count":5,"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":"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":"2511.08173","title":"VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusion","created":"2025-11-11","cited_by_count":3,"area":"mllm"},{"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":"2407.18656","title":"Auto DragGAN: Editing the Generative Image Manifold in an Autoregressive Manner","created":"2024-07-26","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":"2510.16036","title":"IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection","created":"2025-10-16","cited_by_count":2,"area":"mllm"},{"id":"2511.00524","title":"Text-guided Fine-Grained Video Anomaly Understanding","created":"2025-11-01","cited_by_count":2,"area":null},{"id":"2507.16946","title":"Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts","created":"2025-07-22","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":"2601.08022","title":"Training Free Zero-Shot Visual Anomaly Localization via Diffusion Inversion","created":"2026-01-12","cited_by_count":1,"area":"diffusion"},{"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":"2605.30140","title":"AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection","created":"2026-05-28","cited_by_count":1,"area":"mllm"},{"id":"2609.34949","title":"VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection","created":"2026-09-28","cited_by_count":1,"area":"lr"},{"id":"2504.11164","title":"Learning Attribute-aware Representations for Few-shot Scene Text Segmentation","created":"2025-04-15","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":"2506.11772","title":"CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection","created":"2025-06-13","cited_by_count":0,"area":"diffusion"},{"id":"2507.09915","title":"Crucial-Diff: A Unified Diffusion Model for Crucial Image and Annotation Synthesis in Data-scarce Scenarios","created":"2025-07-14","cited_by_count":0,"area":"diffusion"},{"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":"2508.03088","title":"ADSeeker: A Knowledge-Grounded Reasoning Framework for Industry Anomaly Detection and Reasoning","created":"2025-08-05","cited_by_count":0,"area":"lr"},{"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":"2510.22868","title":"Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models","created":"2025-10-26","cited_by_count":0,"area":"mllm"},{"id":"2511.03120","title":"Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning","created":"2025-11-05","cited_by_count":0,"area":null},{"id":"2511.12938","title":"ProtoAnomalyNCD: Prototype Learning for Multi-class Novel Anomaly Discovery in Industrial Scenarios","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2512.09446","title":"Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation","created":"2025-12-10","cited_by_count":0,"area":"mllm"},{"id":"2601.09147","title":"SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection","created":"2026-01-14","cited_by_count":0,"area":"mllm"},{"id":"2603.03101","title":"MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection","created":"2026-03-03","cited_by_count":0,"area":"moe"},{"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":"2605.05850","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":"2605.23231","title":"Beyond Normal References: Discriminative Few-Shot Anomaly Detection","created":"2026-05-22","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":"2606.29714","title":"UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.01049","title":"GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models","created":"2026-07-01","cited_by_count":0,"area":"mllm"}],"next_cursor":null}