Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-
overlapping cameras. With the advancement of deep neural networks and increasing …

Generative adversarial networks (GANs) present a way to learn deep representations
without extensively annotated training data. These networks achieve learning through …

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 …

Many recent methods for unsupervised or self-supervised representation learning train
feature extractors by maximizing an estimate of the mutual information (MI) between different …

This paper considers the domain adaptive person re-identification (re-ID) problem: learning
a re-ID model from a labeled source domain and an unlabeled target domain. Conventional …

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 …

Abstract Domain adaptation in person re-identification (re-ID) has always been a
challenging task. In this work, we explore how to harness the similar natural characteristics …

Person re-identification (re-ID) is an important topic in computer vision. This paper studies
the unsupervised setting of re-ID, which does not require any labeled information and thus is …

Person re-identification (re-ID) models trained on one domain often fail to generalize well to
another. In our attempt, we present a``learning via translation''framework. In the baseline, we …