Device Model Generalization (DMG) is a practical yet under-investigated research topic for
on-device machine learning applications. It aims to improve the generalization ability of pre …

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to
the target domain without any prior knowledge about the label set. The challenge lies in how …

Existing video-audio understanding models are trained and evaluated in an intra-domain
setting, facing performance degeneration in real-world applications where multiple domains …

Abstract Domain shift widely exists in the visual world, while modern deep neural networks
commonly suffer from severe performance degradation under domain shift due to poor …

Considerable progress has been made in domain generalization (DG) which aims to learn a
generalizable model from multiple well-annotated source domains to unknown target …

We introduce a new problem in unsupervised domain adaptation, termed as Generalized
Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target …

Abstract Out-of-Distribution (OOD) Generalization aims to learn robust models that
generalize well to various environments without fitting to distribution-specific features …

Domain generalization (DG) is a prevalent problem in real-world applications, which aims to
train well-generalized models for unseen target domains by utilizing several source …

Abstract arge-scale foundation models, such as CLIP, have demonstrated impressive zero-
shot generalization performance on downstream tasks, leveraging well-designed language …

Federated learning (FL) enables distributed clients to collaboratively learn a global model,
suggesting its potential for use in improving data privacy in machine learning. However …