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 …

Estimating the pose of an uncooperative spacecraft is an important computer vision problem
for enabling the deployment of automatic vision-based systems in orbit, with applications …

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD
algorithms either rely on restricted assumptions or fail to exploit environment information in …

In this paper we make the first attempt at achieving the cross-modal (ie image-to-events)
adaptation for event-based object recognition without accessing any labeled source image …

Knee osteoarthritis is a prevalent disease worldwide. The automatic segmentation of knee
tissues in magnetic resonance (MR) images has important clinical utility in assessing knee …

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables
direct communication between the human brain and a computer. Due to individual …

Medical imaging provides many valuable clues involving anatomical structure and
pathological characteristics. However, image degradation is a common issue in clinical …

Abstract Open-Set Source-Free Domain Adaptation aims to transfer knowledge in realistic
scenarios where the target domain has additional unknown classes compared to the limited …

Test-time adaptation (TTA) addresses distribution shifts for streaming test data in
unsupervised settings. Currently, most TTA methods can only deal with minor shifts and rely …

Abstract Domain adaptation has been widely applied in data-driven fault diagnosis tasks to
address the domain shift problem between source and target data. However, conventional …