  In recent years, supervised person re-identification (re-ID) models have
received increasing studies. However, these models trained on the source domain
always suffer dramatic performance drop when tested on an unseen domain.
Existing methods are primary to use pseudo labels to alleviate this problem.
One of the most successful approaches predicts neighbors of each unlabeled
image and then uses them to train the model. Although the predicted neighbors
are credible, they always miss some hard positive samples, which may hinder the
model from discovering important discriminative information of the unlabeled
domain. In this paper, to complement these low recall neighbor pseudo labels,
we propose a joint learning framework to learn better feature embeddings via
high precision neighbor pseudo labels and high recall group pseudo labels. The
group pseudo labels are generated by transitively merging neighbors of
different samples into a group to achieve higher recall. However, the merging
operation may cause subgroups in the group due to imperfect neighbor
predictions. To utilize these group pseudo labels properly, we propose using a
similarity-aggregating loss to mitigate the influence of these subgroups by
pulling the input sample towards the most similar embeddings. Extensive
experiments on three large-scale datasets demonstrate that our method can
achieve state-of-the-art performance under the unsupervised domain adaptation
re-ID setting.
