  In this paper we propose a novel method for zero-shot, cross-domain image
retrieval in which we make two key contributions. The first is a test-time
re-ranking procedure that enables query-gallery pairs, without meaningful
shared visual features, to be matched by incorporating gallery-gallery ranks
into an iterative re-ranking process. The second is the use of cross-attention
at training time and knowledge distillation to encourage cross-attention-like
features to be extracted at test time from a single image. When combined with
the Vision Transformer architecture and zero-shot retrieval losses, our
approach yields state-of-the-art results on the Sketchy and TU-Berlin
sketch-based image retrieval benchmarks. However, unlike many previous methods,
none of the components in our approach are engineered specifically towards the
sketch-based image retrieval task - it can be generally applied to any
cross-domain, zero-shot retrieval task. We therefore also show results on
zero-shot cartoon-to-photo retrieval using the Office-Home dataset.
