IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
Myr- iad: Large multimodal model by applying vision experts for industrial anomaly detection.arXiv preprint arXiv: 2310.19070
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MMR-AD is a new benchmark dataset showing that current generalist MLLMs lag industrial needs for anomaly detection, with Anomaly-R1 delivering better results through reasoning and RL.
EAGLE achieves up to 94.4% anomaly detection accuracy on MVTec-AD and 88.1% on VisA by guiding frozen MLLMs with expert-derived thresholds and confidence-aware attention without parameter updates.
ForgeryGPT integrates a forgery localization expert and mask encoder into an LLM for pixel-level forgery detection, localization, and explainable output via three-stage training on custom mask-text and instruction datasets.
VLM-based visual anomaly detection for robotic scientific labs via progressive prompt supervision, a new workflow benchmark, and real-world validation showing accuracy gains with added context.
citing papers explorer
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IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation
IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
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MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models
MMR-AD is a new benchmark dataset showing that current generalist MLLMs lag industrial needs for anomaly detection, with Anomaly-R1 delivering better results through reasoning and RL.
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EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models
EAGLE achieves up to 94.4% anomaly detection accuracy on MVTec-AD and 88.1% on VisA by guiding frozen MLLMs with expert-derived thresholds and confidence-aware attention without parameter updates.
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ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization
ForgeryGPT integrates a forgery localization expert and mask encoder into an LLM for pixel-level forgery detection, localization, and explainable output via three-stage training on custom mask-text and instruction datasets.
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A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories
VLM-based visual anomaly detection for robotic scientific labs via progressive prompt supervision, a new workflow benchmark, and real-world validation showing accuracy gains with added context.