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Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3
The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalates the risks of identity fraud and the dissemination of disinformatio…
Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning
Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu +3
With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic face detection methods have a…
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen +1
Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but t…
SURE: Semi-dense Uncertainty-REfined Feature Matching
Sicheng Li, Zaiwang Gu, Jie Zhang +3
Establishing reliable image correspondences is essential for many robotic vision problems. However, existing methods often struggle in challenging scenarios with large viewpoint ch…