While discriminative local features have been shown effective in solving the person re-
identification problem, they are limited to be trained on fully pairwise labelled data which is …

The fundamental difficulty in person re-identification (ReID) lies in learning the
correspondence among individual cameras. It strongly demands costly inter-camera …

This work considers the problem of unsupervised domain adaptation in person re-
identification (re-ID), which aims to transfer knowledge from the source domain to the target …

Drastic variations in illumination across surveillance cameras make the person re-
identification problem extremely challenging. Current large scale re-identification datasets …

Learning an effective distance metric between image pairs plays an important role in visual
analysis, where the training procedure largely relies on hard negative samples. However …

Most existing person re-identification (re-id) methods rely on supervised model learning on
per-camera-pair manually labelled pairwise training data. This leads to poor scalability in a …

Most existing person re-identification (Re-ID) approaches follow a supervised learning
framework, in which a large number of labelled matching pairs are required for training …

Person re-identification (Re-ID) aims to match identities across non-overlapping camera
views. Researchers have proposed many supervised Re-ID models which require quantities …

This paper proposes a self-supervised learning method for the person re-identification (re-
ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as …

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across
different cameras. It is fundamentally challenging because differences between vehicles are …