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.

新闻图像字幕生成的自动化方法通过设计面部命名模块和检索策略以优化视觉信息处理，并超越之前的最佳性能表现。