{"total":271,"rows":[{"id":"2407.15795","title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","created":"2024-07-22","cited_by_count":127,"area":null},{"id":"2403.06495","title":"Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts","created":"2024-03-11","cited_by_count":89,"area":null},{"id":"2503.06661","title":"AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP","created":"2025-03-09","cited_by_count":73,"area":null},{"id":"2401.16402","title":"A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect","created":"2024-01-29","cited_by_count":69,"area":null},{"id":"2403.19949","title":"FairCLIP: Harnessing Fairness in Vision-Language Learning","created":"2024-03-29","cited_by_count":69,"area":null},{"id":"2311.00453","title":"CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection","created":"2023-11-01","cited_by_count":58,"area":null},{"id":"2311.07042","title":"Open-Vocabulary Video Anomaly Detection","created":"2023-11-13","cited_by_count":57,"area":null},{"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":"2405.14529","title":"AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2","created":"2024-05-23","cited_by_count":53,"area":null},{"id":"2404.13671","title":"FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization","created":"2024-04-21","cited_by_count":51,"area":null},{"id":"2407.12276","title":"VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation","created":"2024-07-17","cited_by_count":50,"area":"mllm"},{"id":"2401.12665","title":"ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation","created":"2024-01-23","cited_by_count":49,"area":null},{"id":"2410.09453","title":"MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection","created":"2024-10-12","cited_by_count":47,"area":"mllm"},{"id":"2404.03248","title":"Learning Transferable Negative Prompts for Out-of-Distribution Detection","created":"2024-04-04","cited_by_count":43,"area":null},{"id":"2311.02782","title":"Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead","created":"2023-11-05","cited_by_count":40,"area":null},{"id":"2312.07353","title":"CLIP in Medical Imaging: A Survey","created":"2023-12-12","cited_by_count":40,"area":null},{"id":"2408.05905","title":"Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts","created":"2024-08-12","cited_by_count":40,"area":null},{"id":"2409.09957","title":"Deep Graph Anomaly Detection: A Survey and New Perspectives","created":"2024-09-16","cited_by_count":40,"area":null},{"id":"2503.20188","title":"Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector","created":"2025-03-26","cited_by_count":40,"area":"mllm"},{"id":"2404.09654","title":"Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection","created":"2024-04-15","cited_by_count":36,"area":"mllm"},{"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":"2405.11315","title":"MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection","created":"2024-05-18","cited_by_count":35,"area":null},{"id":"2503.10080","title":"Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection","created":"2025-03-13","cited_by_count":31,"area":"mllm"},{"id":"2410.00320","title":"PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection","created":"2024-10-01","cited_by_count":29,"area":null},{"id":"2505.09926","title":"AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection","created":"2025-05-15","cited_by_count":29,"area":null},{"id":"2410.14886","title":"Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts","created":"2024-10-18","cited_by_count":28,"area":null},{"id":"2504.11914","title":"AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection","created":"2025-04-16","cited_by_count":28,"area":"lr"},{"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":"2409.05383","title":"Deep Learning for Video Anomaly Detection: A Review","created":"2024-09-09","cited_by_count":27,"area":null},{"id":"2410.14987","title":"SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning","created":"2024-10-19","cited_by_count":27,"area":null},{"id":"2503.18325","title":"Towards Training-free Anomaly Detection with Vision and Language Foundation Models","created":"2025-03-24","cited_by_count":26,"area":null},{"id":"2501.01767","title":"LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction","created":"2025-01-03","cited_by_count":25,"area":null},{"id":"2311.02612","title":"GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection","created":"2023-11-05","cited_by_count":23,"area":"mllm"},{"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":"2510.13219","title":"Prompt-based Adaptation in Large-scale Vision Models: A Survey","created":"2025-10-15","cited_by_count":21,"area":null},{"id":"2505.09264","title":"Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt","created":"2025-05-14","cited_by_count":18,"area":null},{"id":"2505.22039","title":"OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning","created":"2025-05-28","cited_by_count":18,"area":"lr"},{"id":"2601.20103","title":"Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis","created":"2026-01-27","cited_by_count":18,"area":"alignment"},{"id":"2501.15795","title":"Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?","created":"2025-01-27","cited_by_count":17,"area":"mllm"},{"id":"2508.09178","title":"IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection","created":"2025-08-07","cited_by_count":17,"area":"lr"},{"id":"2312.10439","title":"Simple Image-level Classification Improves Open-vocabulary Object Detection","created":"2023-12-16","cited_by_count":15,"area":null},{"id":"2402.05355","title":"A Survey on Safe Multi-Modal Learning System","created":"2024-02-08","cited_by_count":15,"area":null},{"id":"2312.11549","title":"Label-Free Multivariate Time Series Anomaly Detection","created":"2023-12-17","cited_by_count":14,"area":null},{"id":"2410.15397","title":"IPO: Interpretable Prompt Optimization for Vision-Language Models","created":"2024-10-20","cited_by_count":14,"area":"mllm"},{"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":"2505.09265","title":"MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning","created":"2025-05-14","cited_by_count":13,"area":"mllm"},{"id":"2412.20392","title":"Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning","created":"2024-12-29","cited_by_count":12,"area":"safety"},{"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":"2505.15205","title":"Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detection","created":"2025-05-21","cited_by_count":12,"area":null},{"id":"2507.21619","title":"EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO","created":"2025-07-29","cited_by_count":12,"area":"lr"},{"id":"2503.14162","title":"EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models","created":"2025-03-18","cited_by_count":11,"area":null},{"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":"2502.20981","title":"Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detection","created":"2025-02-28","cited_by_count":10,"area":null},{"id":"2506.23810","title":"MadCLIP: Few-shot Medical Anomaly Detection with CLIP","created":"2025-06-30","cited_by_count":10,"area":null},{"id":"2508.13560","title":"DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup","created":"2025-08-19","cited_by_count":10,"area":"mllm"},{"id":"2510.00495","title":"Normal-Abnormal Guided Generalist Anomaly Detection","created":"2025-10-01","cited_by_count":10,"area":null},{"id":"2410.19872","title":"Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey","created":"2024-10-24","cited_by_count":9,"area":null},{"id":"2504.12749","title":"LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection","created":"2025-04-17","cited_by_count":9,"area":"lr"},{"id":"2408.00372","title":"Few-shot Defect Image Generation based on Consistency Modeling","created":"2024-08-01","cited_by_count":8,"area":"diffusion"},{"id":"2412.04304","title":"Towards Zero-shot 3D Anomaly Localization","created":"2024-12-05","cited_by_count":8,"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":"2503.13184","title":"Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process","created":"2025-03-17","cited_by_count":8,"area":null},{"id":"2506.05175","title":"Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline","created":"2025-06-05","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":"2602.18793","title":"From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection","created":"2026-02-21","cited_by_count":8,"area":null},{"id":"2602.19248","title":"No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection","created":"2026-02-22","cited_by_count":8,"area":null},{"id":"2409.00556","title":"FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model","created":"2024-08-31","cited_by_count":7,"area":"mllm"},{"id":"2411.17217","title":"Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning","created":"2024-11-26","cited_by_count":7,"area":null},{"id":"2501.03786","title":"KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration","created":"2025-01-07","cited_by_count":7,"area":"mllm"},{"id":"2502.01201","title":"One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection","created":"2025-02-03","cited_by_count":7,"area":null},{"id":"2507.19949","title":"AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation","created":"2025-07-26","cited_by_count":7,"area":null},{"id":"2602.23013","title":"SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling","created":"2026-02-26","cited_by_count":7,"area":null},{"id":"2502.17132","title":"Applications of Large Models in Medicine","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2503.15337","title":"Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport","created":"2025-03-19","cited_by_count":6,"area":null},{"id":"2505.02626","title":"Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models","created":"2025-05-05","cited_by_count":6,"area":"mllm"},{"id":"2602.00522","title":"MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval","created":"2026-01-31","cited_by_count":6,"area":"mllm"},{"id":"2602.09850","title":"Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning","created":"2026-02-10","cited_by_count":6,"area":"lr"},{"id":"2603.14407","title":"Towards One-for-All Anomaly Detection for Tabular Data","created":"2026-03-15","cited_by_count":6,"area":null},{"id":"2409.05611","title":"Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection","created":"2024-09-09","cited_by_count":5,"area":"moe"},{"id":"2410.00713","title":"RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations","created":"2024-10-01","cited_by_count":5,"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":"2504.02775","title":"TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection","created":"2025-04-03","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":"2505.12670","title":"TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning","created":"2025-05-19","cited_by_count":5,"area":"lr"},{"id":"2506.19694","title":"UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation","created":"2025-06-24","cited_by_count":5,"area":null},{"id":"2510.01934","title":"Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors","created":"2025-10-02","cited_by_count":5,"area":null},{"id":"2603.13779","title":"AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison","created":"2026-03-14","cited_by_count":5,"area":"mllm"},{"id":"2405.04782","title":"Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection","created":"2024-05-08","cited_by_count":4,"area":null},{"id":"2406.07333","title":"Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection","created":"2024-06-11","cited_by_count":4,"area":null},{"id":"2408.13516","title":"Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detection","created":"2024-08-24","cited_by_count":4,"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":"2505.10996","title":"Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark","created":"2025-05-16","cited_by_count":4,"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":"2507.07939","title":"SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment","created":"2025-07-10","cited_by_count":4,"area":"mllm"},{"id":"2512.13671","title":"AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation","created":"2025-12-15","cited_by_count":4,"area":"agents"},{"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":"2406.05766","title":"Set-CLIP: Exploring Aligned Semantic From Low-Alignment Multimodal Data Through A Distribution View","created":"2024-06-09","cited_by_count":3,"area":null},{"id":"2411.19623","title":"FairDD: Fair Dataset Distillation","created":"2024-11-29","cited_by_count":3,"area":null}],"next_cursor":"100"}