  Paragraph generation from images, which has gained popularity recently, is an
important task for video summarization, editing, and support of the disabled.
Traditional image captioning methods fall short on this front, since they
aren't designed to generate long informative descriptions. Moreover, the
vanilla approach of simply concatenating multiple short sentences, possibly
synthesized from a classical image captioning system, doesn't embrace the
intricacies of paragraphs: coherent sentences, globally consistent structure,
and diversity. To address those challenges, we propose to augment paragraph
generation techniques with 'coherence vectors', 'global topic vectors', and
modeling of the inherent ambiguity of associating paragraphs with images, via a
variational auto-encoder formulation. We demonstrate the effectiveness of the
developed approach on two datasets, outperforming existing state-of-the-art
techniques on both.
