  Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.
