1 citations · 1 across the 11 of their papers we have counts for
12 papers
DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models
Dingrong Wang, Xian Tao, Zhen Qu +5
Industrial anomaly localization aims to accurately identify and localize abnormal regions in industrial products, addressing the critical challenge of detecting unseen defect categ…
AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models
Zhen Qu, Xian Tao, Xiaoyi Bao +4
Large multimodal models (LMMs) exhibit strong task generalization capabilities, offering new opportunities for zero-shot visual anomaly segmentation (ZSAS). However, existing LMM-b…
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
Chaoran Xu, Chengkan Lv, Qiyu Chen +2
Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data di…
DeltaDeno: Zero-Shot Anomaly Generation via Delta-Denoising Attribution
Chaoran Xu, Chengkan Lv, Qiyu Chen +3
Anomaly generation is often framed as few-shot fine-tuning with anomalous samples, which contradicts the scarcity that motivates generation and tends to overfit category priors. We…
DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup
Zhen Qu, Xian Tao, Xinyi Gong +7
Recent vision-language models (e.g., CLIP) have demonstrated remarkable class-generalizable ability to unseen classes in few-shot anomaly segmentation (FSAS), leveraging supervised…
CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Qiyu Chen, Zhen Qu, Wei Luo +7
Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the mode…