Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Abstract Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems
against presentation attacks. While domain generalization (DG) methods have been …

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

Abstract Domain generalization (DG) is a principal task to evaluate the robustness of
computer vision models. Many previous studies have used normalization for DG. In …

Recent point cloud understanding research suffers from performance drops on unseen data,
due to the distribution shifts across different domains. While recent studies use Domain …

Domain Generalization (DG) aims to develop models that can learn from data in source
domains and generalize to unseen target domains. Recently, some domain generalization …

In the era of the Internet of Things (IoT) objects connect through a dynamic network
empowered by technologies like 5G enabling real-time data sharing. However smart objects …

The rapid development of Vision Foundation Model (VFM) brings inherent out-domain
generalization for a variety of down-stream tasks. Among them, domain generalized …

In domain generalization (DG), the target domain is unknown when the model is being
trained, and the trained model should successfully work on an arbitrary (and possibly …