  In this paper, we build a visual dialogue dataset, named InfoVisDial, which
provides rich informative answers in each round even with external knowledge
related to the visual content. Different from existing datasets where the
answer is compact and short, InfoVisDial contains long free-form answers with
rich information in each round of dialogue. For effective data collection, the
key idea is to bridge the large-scale multimodal model (e.g., GIT) and the
language models (e.g., GPT-3). GIT can describe the image content even with
scene text, while GPT-3 can generate informative dialogue based on the image
description and appropriate prompting techniques. With such automatic pipeline,
we can readily generate informative visual dialogue data at scale. Then, we ask
human annotators to rate the generated dialogues to filter the low-quality
conversations.Human analyses show that InfoVisDial covers informative and
diverse dialogue topics: $54.4\%$ of the dialogue rounds are related to image
scene texts, and $36.7\%$ require external knowledge. Each round's answer is
also long and open-ended: $87.3\%$ of answers are unique with an average length
of $8.9$, compared with $27.37\%$ and $2.9$ in VisDial. Last, we propose a
strong baseline by adapting the GIT model for the visual dialogue task and
fine-tune the model on InfoVisDial. Hopefully, our work can motivate more
effort on this direction.
