The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …

Introduction: There has been a rapid development of deep learning (DL) models for medical
imaging. However, DL requires a large labeled dataset for training the models. Getting large …

We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

This paper studies the problem of learning semantic segmentation from image-level
supervision only. Current popular solutions leverage object localization maps from …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …

There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain
configurations, including closed-set and partial-set DA, as well as multi-source and multi …

Recent advances in adaptive object detection have achieved compelling results in virtue of
adversarial feature adaptation to mitigate the distributional shifts along the detection …

Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled
target set, assuming that the source dataset is available with all labels. However, the …