2 citations · 3 across the 4 of their papers we have counts for
5 papers
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Yingjie Zhou, Yuqin Xie, Fanxing Liu +3
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of…
EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance
Ankit Yadav, Ta Duc Huy, Lingqiao Liu
In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free guidance (CFG) is the de facto choi…
Unified Unsupervised Anomaly Detection via Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Gaochang Wu +5
Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and med…
Revisiting Vision Language Foundations for No-Reference Image Quality Assessment
Ankit Yadav, Ta Duc Huy, Lingqiao Liu
Large-scale vision language pre-training has recently shown promise for no-reference image-quality assessment (NR-IQA), yet the relative merits of modern Vision Transformer foundat…
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi +7
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descript…