Attention mechanisms have been found effective for person re-identification (Re-ID).
However, the learned" attentive" features are often not naturally uncorrelated or" diverse" …

RGB-Infrared (IR) person re-identification is an important and challenging task due to large
cross-modality variations between RGB and IR images. Most conventional approaches aim …

Although unsupervised person re-identification (RE-ID) has drawn increasing research
attentions due to its potential to address the scalability problem of supervised RE-ID models …

Learning discriminative representations for unseen person images is critical for person re-
identification (ReID). Most of the current approaches learn deep representations in …

Since the person re-identification task often suffers from the problem of pose changes and
occlusions, some attentive local features are often suppressed when training CNNs. In this …

Person re-identification (re-id), the process of matching pedestrian images across different
camera views, is an important task in visual surveillance. Substantial development of re-id …

While discriminative local features have been shown effective in solving the person re-
identification problem, they are limited to be trained on fully pairwise labelled data which is …

For matching pedestrians across disjoint camera views in surveillance, person re-
identification (Re-ID) has made great progress in supervised learning. However, it is …

Ubiquitousness of multiple cameras in surveillance systems is very beneficial for studying
peoples behavior. The multiple views of the observed scene permit the management of …

Person re-identification (re-id) aims to match people across non-overlapping camera views
in a public space. This is a challenging problem because the people captured in …