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 …

Although a significant progress has been witnessed in supervised person re-identification
(re-id), it remains challenging to generalize re-id models to new domains due to the huge …

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 …

Existing fully-supervised person re-identification (ReID) methods usually suffer from poor
generalization capability caused by domain gaps. The key to solving this problem lies in …

Abstract Domain adaptive person re-identification (re-ID) is a challenging task, especially
when person identities in target domains are unknown. Existing methods attempt to address …

Often the best performing deep neural models are ensembles of multiple base-level
networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID …

This paper tackles the purely unsupervised person re-identification (Re-ID) problem that
requires no annotations. Some previous methods adopt clustering techniques to generate …

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 …