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 …

Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the
labeled source domain's knowledge to improve the model's discriminability on the unlabeled …

We present a novel unsupervised domain adaption method for person re-identification (reID)
that generalizes a model trained on a labeled source domain to an unlabeled target domain …

Although recent studies empirically show that injecting Convolutional Neural Networks
(CNNs) into Vision Transformers (ViTs) can improve the performance of person re …

Recent studies show that, both explicit deep feature matching as well as large-scale and
diverse training data can significantly improve the generalization of person re-identification …

Unsupervised person re-identification (Re-ID) aims at finding the most informative features
from unlabeled person datasets. Some recent approaches adopted camera-aware …

Language-image pre-training is an effective technique for learning powerful representations
in general domains. However, when directly turning to person representation learning, these …

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 …

We consider a scenario where we have access to the target domain, but cannot afford on-
the-fly training data annotation, and instead would like to construct an alternative training set …