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 …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

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 …

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 …

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 …

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

We propose a novel deep network called Mancs that solves the person re-identification
problem from the following aspects: fully utilizing the attention mechanism for the person …

Long-term re-id with clothes change is a challenging problem in surveillance AI. Currently,
its major bottleneck is that this field is still missing a large realistic benchmark. In this work …

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 …

Person re-identification (Re-ID) is an important problem in video surveillance, aiming to
match pedestrian images across camera views. Currently, most works focus on RGB-based …