  Transferring learned models to novel tasks is a challenging problem,
particularly if only very few labeled examples are available. Although this
few-shot learning setup has received a lot of attention recently, most proposed
methods focus on discriminating novel classes only. Instead, we consider the
extended setup of generalized few-shot learning (GFSL), where the model is
required to perform classification on the joint label space consisting of both
previously seen and novel classes. We propose a graph-based framework that
explicitly models relationships between all seen and novel classes in the joint
label space. Our model Graph-convolutional Global Prototypical Networks (GcGPN)
incorporates these inter-class relations using graph-convolution in order to
embed novel class representations into the existing space of previously seen
classes in a globally consistent manner. Our approach ensures both fast
adaptation and global discrimination, which is the major challenge in GFSL. We
demonstrate the benefits of our model on two challenging benchmark datasets.
