Generative adversarial networks (GANs) present a way to learn deep representations
without extensively annotated training data. These networks achieve learning through …

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 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 …

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 …

Abstract Person Re-identification (ReID) is an important yet challenging task in computer
vision. Due to the diverse background clutters, variations on viewpoints and body poses, it is …

The main contribution of this paper is a simple semi-supervised pipeline that only uses the
original training set without collecting extra data. It is challenging in 1) how to obtain more …

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 …

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 …

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

In this paper, we address the problem of person re-identification, which refers to associating
the persons captured from different cameras. We propose a simple yet effective human part …