  In this paper, we study learning visual classifiers from unstructured text
descriptions at part precision with no training images. We propose a learning
framework that is able to connect text terms to its relevant parts and suppress
connections to non-visual text terms without any part-text annotations. For
instance, this learning process enables terms like "beak" to be sparsely linked
to the visual representation of parts like head, while reduces the effect of
non-visual terms like "migrate" on classifier prediction. Images are encoded by
a part-based CNN that detect bird parts and learn part-specific representation.
Part-based visual classifiers are predicted from text descriptions of unseen
visual classifiers to facilitate classification without training images (also
known as zero-shot recognition). We performed our experiments on CUBirds 2011
dataset and improves the state-of-the-art text-based zero-shot recognition
results from 34.7\% to 43.6\%. We also created large scale benchmarks on North
American Bird Images augmented with text descriptions, where we also show that
our approach outperforms existing methods. Our code, data, and models are
publically available.
