  Existing person re-identification (re-id) methods are stuck when deployed to
a new unseen scenario despite the success in cross-camera person matching.
Recent efforts have been substantially devoted to domain adaptive person re-id
where extensive unlabeled data in the new scenario are utilized in a
transductive learning manner. However, for each scenario, it is required to
first collect enough data and then train such a domain adaptive re-id model,
thus restricting their practical application. Instead, we aim to explore
multiple labeled datasets to learn generalized domain-invariant representations
for person re-id, which is expected universally effective for each new-coming
re-id scenario. To pursue practicability in real-world systems, we collect all
the person re-id datasets (20 datasets) in this field and select the three most
frequently used datasets (i.e., Market1501, DukeMTMC, and MSMT17) as unseen
target domains. In addition, we develop DataHunter that collects over 300K+
weak annotated images named YouTube-Human from YouTube street-view videos,
which joins 17 remaining full labeled datasets to form multiple source domains.
On such a large and challenging benchmark called FastHuman (~440K+ labeled
images), we further propose a simple yet effective Semi-Supervised Knowledge
Distillation (SSKD) framework. SSKD effectively exploits the weakly annotated
data by assigning soft pseudo labels to YouTube-Human to improve models'
generalization ability. Experiments on several protocols verify the
effectiveness of the proposed SSKD framework on domain generalizable person
re-id, which is even comparable to supervised learning on the target domains.
Lastly, but most importantly, we hope the proposed benchmark FastHuman could
bring the next development of domain generalizable person re-id algorithms.
