Abstract Person Re-identification (ReID) is to identify the same person across different
cameras. It is a challenging task due to the large variations in person pose, occlusion …

Typical person re-identification (ReID) methods usually describe each pedestrian with a
single feature vector and match them in a task-specific metric space. However, the methods …

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

Person re-identification (Re-ID) requires discriminative features focusing on the full person
to cope with inaccurate person bounding box detection, background clutter, and occlusion …

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 …

Person re-identification (ReID) focuses on identifying people across different scenes in
video surveillance, which is usually formulated as a binary classification task or a ranking …

In this paper, we propose a novel method called AlignedReID that extracts a global feature
which is jointly learned with local features. Global feature learning benefits greatly from local …

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 …

Part model is one of the key factors to high performance person re-identification (ReID) task.
In recent studies, there are mainly two streams for part model. The first one is to divide a …