3 papers
cs.CV2025
HalluGen: Synthesizing Realistic and Controllable Hallucinations for Evaluating Image Restoration
Seunghoi Kim, Henry F. J. Tregidgo, Chen Jin +2
Generative models are prone to hallucinations: plausible but incorrect structures absent in the ground truth. This issue is problematic in image restoration for safety-critical dom…
eess.IV2025
Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS
Seunghoi Kim, Henry F. J. Tregidgo, Matteo Figini +3
Hallucinations are spurious structures not present in the ground truth, posing a critical challenge in medical image reconstruction, especially for data-driven conditional models.…
cs.CV2024
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model
Mona Sheikh Zeinoddin, Chiara Lena, Jiongqi Qu +11
Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, d…