  In cross-domain retrieval, a model is required to identify images from the
same semantic category across two visual domains. For instance, given a sketch
of an object, a model needs to retrieve a real image of it from an online
store's catalog. A standard approach for such a problem is learning a feature
space of images where Euclidean distances reflect similarity. Even without
human annotations, which may be expensive to acquire, prior methods function
reasonably well using unlabeled images for training. Our problem constraint
takes this further to scenarios where the two domains do not necessarily share
any common categories in training data. This can occur when the two domains in
question come from different versions of some biometric sensor recording
identities of different people. We posit a simple solution, which is to
generate synthetic data to fill in these missing category examples across
domains. This, we do via category preserving translation of images from one
visual domain to another. We compare approaches specifically trained for this
translation for a pair of domains, as well as those that can use large-scale
pre-trained text-to-image diffusion models via prompts, and find that the
latter can generate better replacement synthetic data, leading to more accurate
cross-domain retrieval models. Our best SynCDR model can outperform prior art
by up to 15\%. Code for our work is available at
https://github.com/samarth4149/SynCDR .
