3 papers
cs.CV2026
GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment
Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng +2
Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is to fuse views with sufficient…
cs.CV2026
See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection
Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu +2
In multi-view anomaly detection, more cross-view information can actually hurt. When multiple inspection views are naively fused in a reconstruction-based pipeline, normal cues fro…
cs.CV2025
DirectDrag: High-Fidelity, Mask-Free, Prompt-Free Drag-based Image Editing via Readout-Guided Feature Alignment
Sheng-Hao Liao, Shang-Fu Chen, Tai-Ming Huang +2
Drag-based image editing using generative models provides intuitive control over image structures. However, existing methods rely heavily on manually provided masks and textual pro…