3 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
Shengzhu Yang, Jiawei Du, Jia Guo +4
Subtle semantic differences in retinal image and text data present great challenges for pre-training visual-language models. Moreover, false negative samples, i.e., image-text pair…
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection
Haiming Yao, Yunkang Cao, Wei Luo +3
Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deploym…
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Jia Guo, Shuai Lu, Weihang Zhang +3
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing t…
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
Jiawei Du, Jia Guo, Weihang Zhang +4
The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader…
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
Jia Guo, Haonan Han, Shuai Lu +2
Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes…