  Most state-of-the-art person re-identification (re-id) methods depend on
supervised model learning with a large set of cross-view identity labelled
training data. Even worse, such trained models are limited to only the
same-domain deployment with significantly degraded cross-domain generalization
capability, i.e. "domain specific". To solve this limitation, there are a
number of recent unsupervised domain adaptation and unsupervised learning
methods that leverage unlabelled target domain training data. However, these
methods need to train a separate model for each target domain as supervised
learning methods. This conventional "{\em train once, run once}" pattern is
unscalable to a large number of target domains typically encountered in
real-world deployments. We address this problem by presenting a "train once,
run everywhere" pattern industry-scale systems are desperate for. We formulate
a "universal model learning' approach enabling domain-generic person re-id
using only limited training data of a "{\em single}" seed domain. Specifically,
we train a universal re-id deep model to discriminate between a set of
transformed person identity classes. Each of such classes is formed by applying
a variety of random appearance transformations to the images of that class,
where the transformations simulate the camera viewing conditions of any domains
for making the model training domain generic. Extensive evaluations show the
superiority of our method for universal person re-id over a wide variety of
state-of-the-art unsupervised domain adaptation and unsupervised learning re-id
methods on five standard benchmarks: Market-1501, DukeMTMC, CUHK03, MSMT17, and
VIPeR.
