Similarity has always been a key aspect in computer science and statistics. Any time two
element vectors are compared, many different similarity approaches can be used …

Person re-identification (re-id) remains challenging due to significant intra-class variations
across different cameras. Recently, there has been a growing interest in using generative …

Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …

A family of loss functions built on pair-based computation have been proposed in the
literature which provide a myriad of solutions for deep metric learning. In this pa-per, we …

Persons are often occluded by various obstacles in person retrieval scenarios. Previous
person re-identification (re-id) methods, either overlook this issue or resolve it based on an …

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 …

The combination of global and partial features has been an essential solution to improve
discriminative performances in person re-identification (Re-ID) tasks. Previous part-based …

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

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 …