  Generating natural language questions from visual scenes, known as Visual
Question Generation (VQG), has been explored in the recent past where large
amounts of meticulously labeled data provide the training corpus. However, in
practice, it is not uncommon to have only a few images with question
annotations corresponding to a few types of answers. In this paper, we propose
a new and challenging Few-Shot Visual Question Generation (FS-VQG) task and
provide a comprehensive benchmark to it. Specifically, we evaluate various
existing VQG approaches as well as popular few-shot solutions based on
meta-learning and self-supervised strategies for the FS-VQG task. We conduct
experiments on two popular existing datasets VQG and Visual7w. In addition, we
have also cleaned and extended the VQG dataset for use in a few-shot scenario,
with additional image-question pairs as well as additional answer categories.
We call this new dataset VQG-23. Several important findings emerge from our
experiments, that shed light on the limits of current models in few-shot vision
and language generation tasks. We find that trivially extending existing VQG
approaches with transfer learning or meta-learning may not be enough to tackle
the inherent challenges in few-shot VQG. We believe that this work will
contribute to accelerating the progress in few-shot learning research.
