  This paper considers a realistic problem in person re-identification (re-ID)
task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain
a partial observation of a pedestrian. If we directly compare a partial
pedestrian image with a holistic one, the extreme spatial misalignment
significantly compromises the discriminative ability of the learned
representation. We propose a Visibility-aware Part Model (VPM), which learns to
perceive the visibility of regions through self-supervision. The visibility
awareness allows VPM to extract region-level features and compare two images
with focus on their shared regions (which are visible on both images). VPM
gains two-fold benefit toward higher accuracy for partial re-ID. On the one
hand, compared with learning a global feature, VPM learns region-level features
and benefits from fine-grained information. On the other hand, with visibility
awareness, VPM is capable to estimate the shared regions between two images and
thus suppresses the spatial misalignment. Experimental results confirm that our
method significantly improves the learned representation and the achieved
accuracy is on par with the state of the art.
