5 papers
OSAGEN: Object-Aware Mask Priors and Multistage Decoupled Diffusion for Industrial Anomaly Generation
Jinyi Xu, Peng Chen, Yunkang Cao +3
Industrial anomaly detection and localization are limited by scarce real anomalies and pixel-level annotations, a bottleneck that synthetic image-mask pairs can alleviate. However,…
Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection
Jiaxuan Liu, Yunkang Cao, Yufeng Chen +3
The deployment of zero-shot anomaly detection (AD) in embodied industrial inspection is severely bottlenecked by its reliance on passive, fixed-viewpoint 2D imagery. Such formulati…
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
Peng Chen, Chao Huang, Yunkang Cao +7
Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain d…
M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
Chao Huang, Yanhui Li, Yunkang Cao +5
Although multimodal large language models (MLLMs) have advanced industrial anomaly detection toward a zero-shot paradigm, they still tend to produce high-confidence yet unreliable…
IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Yanhui Li, Yunkang Cao, Chengliang Liu +3
Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific app…