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

Paying Attention to Descriptions Generated by Image Captioning Models