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 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 …

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 …

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an
extremely important and challenging task, which can alleviate the issue of expensive cross …

RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality
retrieval problem, which aims at matching the person-of-interest over visible and infrared …

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 …

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