This paper offers a new perspective to ease the challenge of domain generalization, which
involves maintaining robust results even in unseen environments. Our design focuses on the …

Albeit the notable performance on in-domain test points, it is non-trivial for deep neural
networks to attain satisfactory accuracy when deploying in the open world, where novel …

The generalization capability of machine learning systems degenerates notably when the
test distribution drifts from the training distribution. Recently, Domain Generalization (DG) …

Diabetic retinopathy (DR) is caused by long-standing diabetes and is among the fifth leading
cause for visual impairment. The prospects of early diagnosis and treatment could be helpful …

Deep learning has made significant advancements in supervised learning. However,
models trained in this setting often face challenges due to domain shift between training and …

Test-time adaptation (TTA) is a technique aimed at enhancing the generalization
performance of models by leveraging unlabeled samples solely during prediction. Given the …

Abstract Video-based Unsupervised Domain Adaptation (VUDA) method improves the
generalization of the video model enabling it to be applied to action recognition tasks in …

Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

Domain generalization (DG) aims to train a model with access to a limited number of source
domains for generalizing it across various unseen target domains. The key to solving the DG …

Test-time adaptation (TTA) is a technique to improve the performance of a pre-trained
source model on a target distribution without using any labeled data. However existing self …