To bridge the gap between humans and machines in image understanding and
describing, we need further insight into how people describe a perceived scene.
In this paper, we study the agreement between bottom-up saliency-based visual
attention and object referrals in scene description constructs. We investigate
the properties of human-written descriptions and machine-generated ones. We
then propose a saliency-boosted image captioning model in order to investigate
benefits from low-level cues in language models. We learn that (1) humans
mention more salient objects earlier than less salient ones in their
descriptions, (2) the better a captioning model performs, the better attention
agreement it has with human descriptions, (3) the proposed saliency-boosted
model, compared to its baseline form, does not improve significantly on the MS
COCO database, indicating explicit bottom-up boosting does not help when the
task is well learnt and tuned on a data, (4) a better generalization is,
however, observed for the saliency-boosted model on unseen data.

研究了人类描述场景时自底向上显著性视觉关注和物体引用之间的一致性；提出了一种以显著性为增强因素的图像标题生成模型，结果发现该模型并不明显优于传统方法，但能更好地适用于未知数据。