1 citations · 1 across the 5 of their papers we have counts for
5 papers
ReMoE: Report-Guided Mixture-of-Experts for Multimodal OCT/OCTA Anomaly Detection
Zihan Nie, Qincheng Qiao, Muhao Xu +4
Multimodal medical anomaly detection identifies samples deviating from normal patterns, where scarce abnormal cases make normality modeling from normal data practical. In retinal O…
CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Zihan Nie, Muhao Xu, Wei Feng +8
Medical image anomaly detection is central to timely diagnosis and clinical decision support, yet abnormal samples are costly to collect because of disease rarity, privacy concerns…
MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge
Baochen Fu, Yuntao Du, Cheng Chang +6
As real-world knowledge continues to evolve, the parametric knowledge acquired by multimodal models during pretraining becomes increasingly difficult to remain consistent with real…
Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection
Muhao Xu, Xueying Zhou, Xizhan Gao +3
Recently, detecting logical anomalies is becoming a more challenging task compared to detecting structural ones. Existing encoder decoder based methods typically compress inputs in…
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection
Muhao Xu, Xueying Zhou, Xizhan Gao +2
Unsupervised representation learning has been extensively employed in anomaly detection, achieving impressive performance. Extracting valuable feature vectors that can remarkably i…