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

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

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

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 …

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 …

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 …

Feature extraction and matching are two crucial components in person Re-Identification
(ReID). The large pose deformations and the complex view variations exhibited by the …

Most existing person re-identification (re-id) methods require supervised model learning
from a separate large set of pairwise labelled training data for every single camera pair. This …