14 citations · 17 across the 7 of their papers we have counts for
7 papers
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Shuai Lu, Meng Wang, Jia Guo +6
Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis. However, their clinical deployment is severely hindered by lacking domain-specific k…
Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection
Jia Guo, Shuai Lu, Lei Fan +9
Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the…
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…
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…
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Jia Guo, Shuai Lu, Lize Jia +2
Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. H…