  This paper addresses the problem of cross-domain change detection from a
novel perspective of image-to-image translation. In general, change detection
aims to identify interesting changes between a given query image and a
reference image of the same scene taken at a different time. This problem
becomes a challenging one when query and reference images involve different
domains (e.g., time of the day, weather, and season) due to variations in
object appearance and a limited amount of training examples. In this study, we
address the above issue by leveraging a generative adversarial network (GAN).
Our key concept is to use a limited amount of training data to train a
GAN-based image translator that maps a reference image to a virtual image that
cannot be discriminated from query domain images. This enables us to treat the
cross-domain change detection task as an in-domain image comparison. This
allows us to leverage the large body of literature on in-domain generic change
detectors. In addition, we also consider the use of visual place recognition as
a method for mining more appropriate reference images over the space of virtual
images. Experiments validate efficacy of the proposed approach.
