Deep neural networks (DNNs) have proven explicit contributions in making autonomous
driving cars and related tasks such as semantic segmentation, motion tracking, object …

Adapting a segmentation model from a labeled source domain to a target domain, where a
single unlabeled datum is available, is one of the most challenging problems in domain …

The performance of deep learning models is intrinsically tied to the quality volume and
relevance of their training data. Gathering ample data for production scenarios often …

Deep learning models achieve high accuracy in segmentation tasks among others, yet
domain shift often degrades the models' performance, which can be critical in real-world …

Pre-trained diffusion models have demonstrated remarkable proficiency in synthesizing
images across a wide range of scenarios with customizable prompts, indicating their …

Guidewire segmentation during endovascular interventions holds the potential to
significantly enhance procedural accuracy, improving visualization and providing critical …

Abstract Domain adaptation, the important branch of transfer learning, aims to solve the
problem that the performance of traditional machine learning algorithms drops sharply when …

The supplementary material is organized as follows: Sec. A reports additional experiments
and ablation analysis of our proposed method. Sec. B provides additional implementation …