Customized Image Narrative Generation via
Interactive Visual Question Generation and Answering
Abstract
Image description task has been invariably examined in
a static manner with qualitative presumptions held to be
universally applicable, regardless of the scope or target of
the description. In practice, however, different viewers may
pay attention to different aspects of the image, and yield
different descriptions or interpretations under various contexts. Such diversity in perspectives is difficult to derive
with conventional image description techniques. In this paper, we propose a customized image narrative generation
task, in which the users are interactively engaged in the
generation process by providing answers to the questions.
We further attempt to learn the user’s interest via repeating such interactive stages, and to automatically reflect the
interest in descriptions for new images. Experimental results demonstrate that our model can generate a variety of
descriptions from single image that cover a wider range of
topics than conventional models, while being customizable
to the target user of interaction