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 …

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem,
which aims at matching the same pedestrian between the visible and infrared cameras. Due …

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

Recent advances in person re-identification (ReID) obtain impressive accuracy in the
supervised and unsupervised learning settings. However, most of the existing methods need …

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 …

Person re-identification (re-ID) is an important topic in computer vision. This paper studies
the unsupervised setting of re-ID, which does not require any labeled information and thus is …

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

Long-term re-id with clothes change is a challenging problem in surveillance AI. Currently,
its major bottleneck is that this field is still missing a large realistic benchmark. In this work …