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 …

Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

Existing alignment-based methods have to employ the pre-trained human parsing models to
achieve the pixel-level alignment, and cannot identify the personal belongings (eg …

As an instance-level recognition problem, person re-identification (ReID) relies on
discriminative features, which not only capture different spatial scales but also encapsulate …

In the task of person re-identification, the attention mechanism and fine-grained information
have been proved to be effective. However, it has been observed that models often focus on …

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 …

For person re-identification (re-id), attention mechanisms have become attractive as they
aim at strengthening discriminative features and suppressing irrelevant ones, which …

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 …

Abstract Infrared-Visible person RE-IDentification (IV-REID) is a rising task. Compared to
conventional person re-identification (re-ID), IV-REID concerns the additional modality …

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