Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …

Adaptive person re-identification (adaptive ReID) targets at transferring learned knowledge
from the labeled source domain to the unlabeled target domain. Pseudo-label-based …

This paper aims to learn a domain-generalizable (DG) person re-identification (ReID)
representation from large-scale videos without any annotation. Prior DG ReID methods …

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of
transferring the learned knowledge on the labeled source domain to the unlabeled target …

Unsupervised person re-identification (re-ID) remains a challenging task. While extensive
research has focused on the framework design and loss function, this paper shows that …

Multi-grained features extracted from convolutional neural networks (CNNs) have
demonstrated their strong discrimination ability in supervised person re-identification (Re-ID) …

Although the single-domain person re-identification (Re-ID) method has achieved great
accuracy, the dependence on the label in the same image domain severely limits the …

Near-infrared and visible face recognition (NIR-VIS) is attracting increasing attention
because of the need to achieve face recognition in low-light conditions to enable 24-hour …

Abstract Domain generalization (DG) has attracted much attention in person re-identification
(ReID) recently. It aims to make a model trained on multiple source domains generalize to …