Unsupervised domain adaptation (UDA) and domain generalization (DG) enable machine
learning models trained on a source domain to perform well on unlabeled or even unseen …

Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability
to generate creative content, specialize to user data through few-shot fine-tuning, and …

Abstract Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap
between a labeled source domain, eg, synthetic data, and an unlabeled target domain. We …

In unsupervised domain adaptation (UDA), where models are trained on source data (eg,
synthetic) and adapted to target data (eg, real-world) without target annotations, addressing …

Panoramic images can broaden the Field of View (FoV), occlusion-aware prediction can
deepen the understanding of the scene, and domain adaptation can transfer across viewing …

Abstract Domain adaptive panoptic segmentation promises to resolve the long tail of corner
cases in natural scene understanding. Previous state of the art addresses this problem with …

Merging parameters of multiple models has resurfaced as an effective strategy to enhance
task performance and robustness, but prior work is limited by the high costs of ensemble …

Recent advancements in retinal vessel segmentation, which employ transformer-based and
domain-adaptive approaches, show promise in addressing the complexity of ocular …

The increasing relevance of panoptic segmentation is tied to the advancements in
autonomous driving and AR/VR applications. However, the deployment of such models has …

Unsupervised Domain Adaptation (UDA) endeavors to adjust models trained on a source
domain to perform well on a target domain without requiring additional annotations. In the …