  We exploit field guides to learn bird species recognition, in particular
zero-shot recognition of unseen species. Illustrations contained in field
guides deliberately focus on discriminative properties of each species, and can
serve as side information to transfer knowledge from seen to unseen bird
species. We study two approaches: (1) a contrastive encoding of illustrations,
which can be fed into standard zero-shot learning schemes; and (2) a novel
method that leverages the fact that illustrations are also images and as such
structurally more similar to photographs than other kinds of side information.
Our results show that illustrations from field guides, which are readily
available for a wide range of species, are indeed a competitive source of side
information for zero-shot learning. On a subset of the iNaturalist2021 dataset
with 749 seen and 739 unseen species, we obtain a classification accuracy of
unseen bird species of $12\%$ @top-1 and $38\%$ @top-10, which shows the
potential of field guides for challenging real-world scenarios with many
species. Our code is available at https://github.com/ac-rodriguez/zsl_billow
