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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…
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…
cs.CV2024
Tackling Structural Hallucination in Image Translation with Local Diffusion
Seunghoi Kim, Chen Jin, Tom Diethe +5
Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in…