  Most current image captioning systems focus on describing general image
content, and lack background knowledge to deeply understand the image, such as
exact named entities or concrete events. In this work, we focus on the
entity-aware news image captioning task which aims to generate informative
captions by leveraging the associated news articles to provide background
knowledge about the target image. However, due to the length of news articles,
previous works only employ news articles at the coarse article or sentence
level, which are not fine-grained enough to refine relevant events and choose
named entities accurately. To overcome these limitations, we propose an
Information Concentrated Entity-aware news image CAPtioning (ICECAP) model,
which progressively concentrates on relevant textual information within the
corresponding news article from the sentence level to the word level. Our model
first creates coarse concentration on relevant sentences using a cross-modality
retrieval model and then generates captions by further concentrating on
relevant words within the sentences. Extensive experiments on both BreakingNews
and GoodNews datasets demonstrate the effectiveness of our proposed method,
which outperforms other state-of-the-arts. The code of ICECAP is publicly
available at https://github.com/HAWLYQ/ICECAP.

Computer Vision and Pattern Recognition;Multimedia