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 …

In the evolving landscape of surveillance and security applications, the task of person re-
identification (re-ID) has significant importance, but also presents notable difficulties. This …

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 …

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 …

Unsupervised visible-infrared person re-identification (US-VI-ReID) centers on learning a
cross-modality retrieval model without labels reducing the reliance on expensive cross …

Abstract Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training. Almost …

Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-
based pseudo-label prediction with feature fine-tuning. However, because of domain gap …

Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for
contrastive learning. However, due to the intra-class variance and inter-class similarity, the …

Re-identification (re-ID) aims to identify a person's images across different cameras.
However, the domain differences between different datasets make it a challenge for re-ID …

Person re-identification (Re-ID) has achieved great success in single-domain. However, it
remains a challenging task to adapt a Re-ID model trained on one dataset to another one …