  Vision is a common source of inspiration for poetry. The objects and the
sentimental imprints that one perceives from an image may lead to various
feelings depending on the reader. In this paper, we present a system of poetry
generation from images to mimic the process. Given an image, we first extract a
few keywords representing objects and sentiments perceived from the image.
These keywords are then expanded to related ones based on their associations in
human written poems. Finally, verses are generated gradually from the keywords
using recurrent neural networks trained on existing poems. Our approach is
evaluated by human assessors and compared to other generation baselines. The
results show that our method can generate poems that are more artistic than the
baseline methods. This is one of the few attempts to generate poetry from
images. By deploying our proposed approach, XiaoIce has already generated more
than 12 million poems for users since its release in July 2017. A book of its
poems has been published by Cheers Publishing, which claimed that the book is
the first-ever poetry collection written by an AI in human history.
