Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to
health-care organizations worldwide. To combat the global crisis, the use of thoracic …

Pre-trained vision-language models (eg, CLIP) have shown promising zero-shot
generalization in many downstream tasks with properly designed text prompts. Instead of …

Test-time adaptation is a special setting of unsupervised domain adaptation where a trained
model on the source domain has to adapt to the target domain without accessing source …

Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …

Abstract Domain generalization (DG) aims to generalize a model trained on multiple source
(ie, training) domains to a distributionally different target (ie, test) domain. In contrast to the …

A model must adapt itself to generalize to new and different data during testing. In this
setting of fully test-time adaptation the model has only the test data and its own parameters …

This paper focuses on domain generalization (DG), the task of learning from multiple source
domains a model that generalizes well to unseen domains. A main challenge for DG is that …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

Adapting to a continuously evolving environment is a safety-critical challenge inevitably
faced by all autonomous-driving systems. Existing image-and video-based driving datasets …