  Vehicle re-identification (reID) aims at identifying vehicles across
different non-overlapping cameras views. The existing methods heavily relied on
well-labeled datasets for ideal performance, which inevitably causes fateful
drop due to the severe domain bias between the training domain and the
real-world scenes; worse still, these approaches required full annotations,
which is labor-consuming. To tackle these challenges, we propose a novel
progressive adaptation learning method for vehicle reID, named PAL, which
infers from the abundant data without annotations. For PAL, a data adaptation
module is employed for source domain, which generates the images with similar
data distribution to unlabeled target domain as ``pseudo target samples''.
These pseudo samples are combined with the unlabeled samples that are selected
by a dynamic sampling strategy to make training faster. We further proposed a
weighted label smoothing (WLS) loss, which considers the similarity between
samples with different clusters to balance the confidence of pseudo labels.
Comprehensive experimental results validate the advantages of PAL on both
VehicleID and VeRi-776 dataset.
