Unsupervised person re-identification (Re-ID) aims to learn discriminative features without
human-annotated labels. Recently, contrastive learning has provided a new prospect for …

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from
different camera views in an unsupervised setting. Existing methods for unsupervised …

Unsupervised person re-identification (Re-ID) remains challenging due to the lack of ground-
truth labels. Existing methods often rely on estimated pseudo labels via iterative clustering …

The goal of unsupervised person re-identification (Re-ID) is to use unlabeled person images
to learn discriminative features. In recent years, many approaches have adopted clustered …

Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled
source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set …

Identifying individuals in unconstrained video settings is a valuable yet challenging task in
biometric analysis due to variations in appearances, environments, degradations, and …

Person re-identification (re-ID) is a challenging task that aims to learn discriminative features
for person retrieval. In person re-ID Jaccard distance is a widely used distance metric …

Although unsupervised person re-identification (Re-ID) has drawn increasing research
attention recently, it remains challenging to learn discriminative features without annotations …

ABSTRACT Re-identification (Re-ID) is a process that seeks to identify concern individuals
from successive non-overlapping photographs. The area of computer vision has recently …

Unsupervised person re-identification (Re-ID) is to retrieve pedestrians from different
camera views without supervision information. State-of-the-art methods are usually built …