8 papers
FiRe: Fixed-Noise Refinement for Visual Counterfactual Explanations
Yan Zeng, Changlu Guo, Oskar Kristoffersen +3
Visual counterfactual explanations aim to change classifier decisions through realistic and localized edits while preserving decision-irrelevant content. Existing DDPM-based method…
Materialist: Physically Based Editing Using Single-Image Inverse Rendering
Lezhong Wang, Duc Minh Tran, Ruiqi Cui +5
Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on neural networks, which struggle t…
MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations
Changlu Guo, Anders Nymark Christensen, Anders Bjorholm Dahl +1
Visual counterfactual explanations aim to reveal the minimal semantic modifications that can alter a model's prediction, providing causal and interpretable insights into deep neura…
MozzaVID: Mozzarella Volumetric Image Dataset
Pawel Tomasz Pieta, Peter Winkel Rasmussen, Anders Bjorholm Dahl +4
Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and e…
SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation
Changlu Guo, Anders Nymark Christensen, Anders Bjorholm Dahl +2
Retinal vessel segmentation is essential for early diagnosis of diseases such as diabetic retinopathy, hypertension, and neurodegenerative disorders. Although SA-UNet introduces sp…
ReLumix: Extending Image Relighting to Video via Video Diffusion Models
Lezhong Wang, Shutong Jin, Ruiqi Cui +3
Controlling illumination during video post-production is a crucial yet elusive goal in computational photography. Existing methods often lack flexibility, restricting users to cert…