The use of attention models for automated image captioning has enabled many
systems to produce accurate and meaningful descriptions for images. Over the
years, many novel approaches have been proposed to enhance the attention
process using different feature representations. In this paper, we extend this
approach by creating a guided attention network mechanism, that exploits the
relationship between the visual scene and text-descriptions using spatial
features from the image, high-level information from the topics, and temporal
context from caption generation, which are embedded together in an ordered
embedding space. A pairwise ranking objective is used for training this
embedding space which allows similar images, topics and captions in the shared
semantic space to maintain a partial order in the visual-semantic hierarchy and
hence, helps the model to produce more visually accurate captions. The
experimental results based on MSCOCO dataset shows the competitiveness of our
approach, with many state-of-the-art models on various evaluation metrics.

本文提出了一种引导式的注意力网络机制，将图像的空间特征、主题的高级信息以及生成的字幕的时间上下文嵌入到一个有序的嵌入空间中进行训练，采用成对的排序目标函数，该模型在 MSCOCO 数据集上表现出与众多最先进模型相媲美的竞争力。