Unsupervised domain adaption (UDA) aims to reduce the domain gap between labeled
source and unlabeled target domains. Many prior works exploit adversarial learning that …

Abstract Domain generalization (DG) person re-identification (Re-ID) with a good
generalization ability on unseen domains has received widespread attention. Existing DG …

Advancements in adaptive object detection can lead to tremendous improvements in
applications like autonomous navigation, as they alleviate the distributional shifts along the …

In unsupervised domain adaptation (UDA), many efforts are taken to pull the source domain
and the target domain closer by adversarial training. Most methods focus on aligning …

Great progress has been achieved in domain adaptation in decades. Existing works are
always based on an ideal assumption that testing target domains are independent and …

无监督跨域的行人重识别旨在将从有标签的源域中学**到的知识迁移到无标签的目标域,
具有实用性和有效性而得到广泛关注. 基于聚类的跨域行人重识别可以生成伪标签并对模型进行 …

Re-identification of individuals (ReID) involves matching images of pedestrians captured by
a network of multiple cameras that do not capture conditions or fields of view. Given a query …