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 …

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 …

Thanks to the recent research development in contrastive learning, the gap of visual
representation learning between supervised and unsupervised approaches has been …

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 …

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 …

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 …