  Humans are capable of learning a new fine-grained concept with very little
supervision, \emph{e.g.}, few exemplary images for a species of bird, yet our
best deep learning systems need hundreds or thousands of labeled examples. In
this paper, we try to reduce this gap by studying the fine-grained image
recognition problem in a challenging few-shot learning setting, termed few-shot
fine-grained recognition (FSFG). The task of FSFG requires the learning systems
to build classifiers for novel fine-grained categories from few examples (only
one or less than five). To solve this problem, we propose an end-to-end
trainable deep network which is inspired by the state-of-the-art fine-grained
recognition model and is tailored for the FSFG task.
  Specifically, our network consists of a bilinear feature learning module and
a classifier mapping module: while the former encodes the discriminative
information of an exemplar image into a feature vector, the latter maps the
intermediate feature into the decision boundary of the novel category. The key
novelty of our model is a "piecewise mappings" function in the classifier
mapping module, which generates the decision boundary via learning a set of
more attainable sub-classifiers in a more parameter-economic way. We learn the
exemplar-to-classifier mapping based on an auxiliary dataset in a meta-learning
fashion, which is expected to be able to generalize to novel categories. By
conducting comprehensive experiments on three fine-grained datasets, we
demonstrate that the proposed method achieves superior performance over the
competing baselines.
