  Existing person re-identification models often have low generalizability,
which is mostly due to limited availability of large-scale labeled data in
training. However, labeling large-scale training data is very expensive and
time-consuming, while large-scale synthetic dataset shows promising value in
learning generalizable person re-identification models. Therefore, in this
paper a novel and practical person re-identification task is proposed,i.e. how
to use labeled synthetic dataset and unlabeled real-world dataset to train a
universal model. In this way, human annotations are no longer required, and it
is scalable to large and diverse real-world datasets. To address the task, we
introduce a framework with high generalizability, namely DomainMix.
Specifically, the proposed method firstly clusters the unlabeled real-world
images and selects the reliable clusters. During training, to address the large
domain gap between two domains, a domain-invariant feature learning method is
proposed, which introduces a new loss,i.e. domain balance loss, to conduct an
adversarial learning between domain-invariant feature learning and domain
discrimination, and meanwhile learns a discriminative feature for person
re-identification. This way, the domain gap between synthetic and real-world
data is much reduced, and the learned feature is generalizable thanks to the
large-scale and diverse training data. Experimental results show that the
proposed annotation-free method is more or less comparable to the counterpart
trained with full human annotations, which is quite promising. In addition, it
achieves the current state of the art on several person re-identification
datasets under direct cross-dataset evaluation.
