In a typical real-world application of re-id, a watch-list (gallery set) of a handful of target
people (eg suspects) to track around a large volume of non-target people are demanded …

The attention mechanism can extract salient features in images, which has been proved to
be effective in improving the performance of person re-identification (Re-ID). However, most …

In recent years, person re-identification (Re-ID) as a widely studied computer vision task,
has reached a saturation state under closed-world setting, which encourages researchers to …

In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset"
LUPerson" and make the first attempt of performing unsupervised pre-training for improving …

Person re-identification (re-id) is an autonomous process that uses raw surveillance images
to identify a person across multiple non-overlapping camera views without requiring any …

Finding the same individual across cameras in disjoint views at different locations and times,
which is known as person re-identification (re-id), is an important but difficult task in …

Person re-identification (re-id) is a promising topic in computer vision, which concentrates on
similarity learning of individuals across different camera views. It remains challenging due to …

Aiming at the problem of low accuracy of person re-identification (Re-ID) algorithm caused
by occlusion, low distinctiveness of person features and unclear detail features in complex …

Most person re-identification (re-ID) approaches are based on supervised learning, which
requires intensive manual annotation for training data. However, it is not only …

A series of unsupervised video-based re-identification (re-ID) methods have been proposed
to solve the problem of high labor cost required to annotate re-ID datasets. But their …