ICECAP: Information Concentrated Entity-aware Image Captioning

TL;DR

ICECAP is a novel image captioning model that leverages news articles at multiple levels of detail to generate more informative and entity-aware captions, outperforming existing methods.

Contribution

The paper introduces ICECAP, a progressive concentration approach that refines relevant news information from sentence to word level for improved image captioning.

Findings

Outperforms state-of-the-art methods on BreakingNews and GoodNews datasets

Effectively concentrates on relevant textual information at multiple levels

Demonstrates significant improvements in caption informativeness and accuracy

Abstract

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.