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Visual News: Benchmark and Challenges in News Image Captioning

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arxiv 2010.03743 v3 pith:GOB26I4F submitted 2020-10-08 cs.CV

Fuxiao Liu , Yinghan Wang , Tianlu Wang , Vicente Ordonez This is my paper · ORCID
classification cs.CV
keywords newsvisualcaptioningimageimagesbenchmarkcaptionschallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose Visual News Captioner, an entity-aware model for the task of news image captioning. We also introduce Visual News, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata. Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance. Our proposed method can effectively combine visual and textual features to generate captions with richer information such as events and entities. More specifically, built upon the Transformer architecture, our model is further equipped with novel multi-modal feature fusion techniques and attention mechanisms, which are designed to generate named entities more accurately. Our method utilizes much fewer parameters while achieving slightly better prediction results than competing methods. Our larger and more diverse Visual News dataset further highlights the remaining challenges in captioning news images.

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Cited by 3 Pith papers

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