Visually-Aware Context Modeling for News Image Captioning
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The goal of News Image Captioning is to generate an image caption according to the content of both a news article and an image. To leverage the visual information effectively, it is important to exploit the connection between the context in the articles/captions and the images. Psychological studies indicate that human faces in images draw higher attention priorities. On top of that, humans often play a central role in news stories, as also proven by the face-name co-occurrence pattern we discover in existing News Image Captioning datasets. Therefore, we design a face-naming module for faces in images and names in captions/articles to learn a better name embedding. Apart from names, which can be directly linked to an image area (faces), news image captions mostly contain context information that can only be found in the article. Humans typically address this by searching for relevant information from the article based on the image. To emulate this thought process, we design a retrieval strategy using CLIP to retrieve sentences that are semantically close to the image. We conduct extensive experiments to demonstrate the efficacy of our framework. Without using additional paired data, we establish the new state-of-the-art performance on two News Image Captioning datasets, exceeding the previous state-of-the-art by 5 CIDEr points. We will release code upon acceptance.
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