Person re-identification aims to retrieve persons in highly varying settings across different
cameras and scenarios, in which robust and discriminative representation learning is …

Cross-modality person re-identification is a challenging task due to large cross-modality
discrepancy and intra-modality variations. Currently, most existing methods focus on …

Existing person re-identification (re-id) methods either assume the availability of well-
aligned person bounding box images as model input or rely on constrained attention …

Attention has become more attractive in person re-identification (ReID) as it is capable of
biasing the allocation of available resources towards the most informative parts of an input …

Person re-identification (reID) benefits greatly from deep convolutional neural networks
(CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in …

In the past few years, the field of computer vision has gone through a revolution fueled
mainly by the advent of large datasets and the adoption of deep convolutional neural …

Comparing the appearance of corresponding body parts is essential for person re-
identification. As body parts are frequently misaligned between the detected human boxes …

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 …

Person re-identification (ReID) is an important task in video surveillance and has various
applications. It is non-trivial due to complex background clutters, varying illumination …