The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

Conventional machine learning relies on two presumptions:(1) the training and testing
datasets follow the same independent distribution, and (2) an adequate quantity of samples …

Unsupervised domain adaptation has attracted appealing academic attentions by
transferring knowledge from labeled source domain to unlabeled target domain. However …

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for
the target domain, in which only unlabeled and a small number of labeled data are …

Unsupervised domain adaptive classifcation intends to improve the classifcation
performance on unlabeled target domain. To alleviate the adverse effect of domain shift …

Traditional supervised learning aims to train a classifier in the closed-set world, where
training and test samples share the same label space. In this paper, we target a more …

Skeleton sequences are lightweight and compact and thus are ideal candidates for action
recognition on edge devices. Recent skeleton-based action recognition methods extract …

In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with
massive true-label data from the source domain and unlabeled data from the target domain …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …