Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Abstract Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at
adapting the model trained on a labeled source-domain dataset to a target-domain dataset …

Most existing unsupervised person re-identification (Re-ID) methods use clustering to
generate pseudo labels for model training. Unfortunately, clustering sometimes mixes …

Recently, person re-identification (Re-ID) has achieved great progress. However, current
methods largely depend on color appearance, which is not reliable when a person changes …

Recent self-supervised contrastive learning provides an effective approach for unsupervised
person re-identification (ReID) by learning invariance from different views (transformed …

Unsupervised domain adaptation (UDA) in the task of person re-identification (re-ID) is
highly challenging due to large domain divergence and no class overlap between domains …

Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-
based pseudo-label prediction with feature fine-tuning. However, because of domain gap …

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 considers the problem of unsupervised person re-identification (re-ID), which
aims to learn discriminative models with unlabeled data. One popular method is to obtain …

Unsupervised person re-identification (Re-ID) aims to learn discriminative features without
human-annotated labels. Recently, contrastive learning has provided a new prospect for …