  Fine-grained zero-shot learning task requires some form of side-information
to transfer discriminative information from seen to unseen classes. As manually
annotated visual attributes are extremely costly and often impractical to
obtain for a large number of classes, in this study we use DNA as side
information for the first time for fine-grained zero-shot classification of
species. Mitochondrial DNA plays an important role as a genetic marker in
evolutionary biology and has been used to achieve near-perfect accuracy in the
species classification of living organisms. We implement a simple hierarchical
Bayesian model that uses DNA information to establish the hierarchy in the
image space and employs local priors to define surrogate classes for unseen
ones. On the benchmark CUB dataset, we show that DNA can be equally promising
yet in general a more accessible alternative than word vectors as a side
information. This is especially important as obtaining robust word
representations for fine-grained species names is not a practicable goal when
information about these species in free-form text is limited. On a newly
compiled fine-grained insect dataset that uses DNA information from over a
thousand species, we show that the Bayesian approach outperforms
state-of-the-art by a wide margin.
