  Due to domain bias, directly deploying a deep person re-identification
(re-ID) model trained on one dataset often achieves considerably poor accuracy
on another dataset. In this paper, we propose an Adaptive Exploration (AE)
method to address the domain-shift problem for re-ID in an unsupervised manner.
Specifically, in the target domain, the re-ID model is inducted to 1) maximize
distances between all person images and 2) minimize distances between similar
person images. In the first case, by treating each person image as an
individual class, a non-parametric classifier with a feature memory is
exploited to encourage person images to move far away from each other. In the
second case, according to a similarity threshold, our method adaptively selects
neighborhoods for each person image in the feature space. By treating these
similar person images as the same class, the non-parametric classifier forces
them to stay closer. However, a problem of the adaptive selection is that, when
an image has too many neighborhoods, it is more likely to attract other images
as its neighborhoods. As a result, a minority of images may select a large
number of neighborhoods while a majority of images have only a few
neighborhoods. To address this issue, we additionally integrate a balance
strategy into the adaptive selection. We evaluate our methods with two
protocols. The first one is called "target-only re-ID", in which only the
unlabeled target data is used for training. The second one is called "domain
adaptive re-ID", in which both the source data and the target data are used
during training. Experimental results on large-scale re-ID datasets demonstrate
the effectiveness of our method. Our code has been released at
https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.
