Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Abstract Domain adaptive object re-ID aims to transfer the learned knowledge from the
labeled source domain to the unlabeled target domain to tackle the open-class re …

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …

Unsupervised person re-identification (ReID) aims at learning discriminative identity features
without annotations. Recently, self-supervised contrastive learning has gained increasing …

Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by
measuring the feature similarity without considering the distribution discrepancy among …

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

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 …

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

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

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