4 papers
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Jakob Lønborg Christensen, Jakob Lønborg Christensen, Vedrana Andersen Dahl +3
Uncertainty quantification (UQ) is crucial in safety-critical applications such as medical image segmentation. Total uncertainty is typically decomposed into data-related aleatoric…
Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusion
Jakob Lønborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl +1
This paper introduces a diffusion-based framework for universal image segmentation, making agnostic segmentation possible without depending on mask-based frameworks and instead pre…
Diffusion Based Ambiguous Image Segmentation
Jakob Lønborg Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl +1
Medical image segmentation often involves inherent uncertainty due to variations in expert annotations. Capturing this uncertainty is an important goal and previous works have used…
Two Views Are Better than One: Monocular 3D Pose Estimation with Multiview Consistency
Christian Keilstrup Ingwersen, Rasmus Tirsgaard, Rasmus Nylander +3
Deducing a 3D human pose from a single 2D image is inherently challenging because multiple 3D poses can correspond to the same 2D representation. 3D data can resolve this pose ambi…