  Modern recognition systems require large amounts of supervision to achieve
accuracy. Adapting to new domains requires significant data from experts, which
is onerous and can become too expensive. Zero-shot learning requires an
annotated set of attributes for a novel category. Annotating the full set of
attributes for a novel category proves to be a tedious and expensive task in
deployment. This is especially the case when the recognition domain is an
expert domain. We introduce a new field-guide-inspired approach to zero-shot
annotation where the learner model interactively asks for the most useful
attributes that define a class. We evaluate our method on classification
benchmarks with attribute annotations like CUB, SUN, and AWA2 and show that our
model achieves the performance of a model with full annotations at the cost of
a significantly fewer number of annotations. Since the time of experts is
precious, decreasing annotation cost can be very valuable for real-world
deployment.
