Edge segmentation is well-known to be subjective due to personalized annotation styles
and preferred granularity. However most existing deterministic edge detection methods …

A major challenge in the segmentation of medical images is the large inter-and intra-
observer variability in annotations provided by multiple experts. To address this challenge …

Abstract Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D
images which is a challenging task due to inherent ambiguities in translating 2D …

Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in
medical scans and different observer expertise and preferences has become a major …

Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving
impressive results in learn-based LiDAR localization. However compared to the top …

Existing learning-based solutions to medical image segmentation have two important
shortcomings. First for most new segmentation tasks a new model has to be trained or fine …

In the current era, diffusion models have emerged as a groundbreaking force in the realm of
medical image segmentation. Against this backdrop, we introduce the Diffusion Text …

In this study, we introduce “instance loss functions”, a new family of loss functions designed
to enhance the training of neural networks in the instance-level segmentation and detection …

Blood vessel segmentation in medical imaging is one of the essential steps for vascular
disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in …

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the
high similarity between camouflaged objects and their surroundings. Existing COD methods …