  This paper studies the problem of zero-short sketch-based image retrieval
(ZS-SBIR), however with two significant differentiators to prior art (i) we
tackle all variants (inter-category, intra-category, and cross datasets) of
ZS-SBIR with just one network (``everything''), and (ii) we would really like
to understand how this sketch-photo matching operates (``explainable''). Our
key innovation lies with the realization that such a cross-modal matching
problem could be reduced to comparisons of groups of key local patches -- akin
to the seasoned ``bag-of-words'' paradigm. Just with this change, we are able
to achieve both of the aforementioned goals, with the added benefit of no
longer requiring external semantic knowledge. Technically, ours is a
transformer-based cross-modal network, with three novel components (i) a
self-attention module with a learnable tokenizer to produce visual tokens that
correspond to the most informative local regions, (ii) a cross-attention module
to compute local correspondences between the visual tokens across two
modalities, and finally (iii) a kernel-based relation network to assemble local
putative matches and produce an overall similarity metric for a sketch-photo
pair. Experiments show ours indeed delivers superior performances across all
ZS-SBIR settings. The all important explainable goal is elegantly achieved by
visualizing cross-modal token correspondences, and for the first time, via
sketch to photo synthesis by universal replacement of all matched photo
patches. Code and model are available at
\url{https://github.com/buptLinfy/ZSE-SBIR}.

Computer Vision and Pattern Recognition