Employing part-level features offers fine-grained information for pedestrian image
description. A prerequisite of part discovery is that each part should be well located. Instead …

In this paper, we introduce Random Erasing, a new data augmentation method for training
the convolutional neural network (CNN). In training, Random Erasing randomly selects a …

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a
critical step to improve its accuracy. Yet in the re-ID community, limited effort has been …

Person re-identification (ReID) is an important task in wide area video surveillance which
focuses on identifying people across different cameras. Recently, deep learning networks …

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 …

The fundamental challenge of small inter-person variation requires Person Re-Identification
(Re-ID) models to capture sufficient fine-grained information. This paper proposes to …

This paper aims to address the problem of pre-training for person re-identification (Re-ID)
with noisy labels. To setup the pre-training task, we apply a simple online multi-object …

Part-level features offer fine granularity for pedestrian image description. In this article, we
generally aim to learn discriminative part-informed feature for person re-identification. Our …

In the past decade, research in person re-identification (re-id) has exploded due to its broad
use in security and surveillance applications. Issues such as inter-camera viewpoint …

Person retrieval largely relies on the appearance features of pedestrians. This task is rather
more difficult in surveillance videos due to the limitations of extracting robust appearance …