This paper presents a new metric called TIGEr for the automatic evaluation of
image captioning systems. Popular metrics, such as BLEU and CIDEr, are based
solely on text matching between reference captions and machine-generated
captions, potentially leading to biased evaluations because references may not
fully cover the image content and natural language is inherently ambiguous.
Building upon a machine-learned text-image grounding model, TIGEr allows to
evaluate caption quality not only based on how well a caption represents image
content, but also on how well machine-generated captions match human-generated
captions. Our empirical tests show that TIGEr has a higher consistency with
human judgments than alternative existing metrics. We also comprehensively
assess the metric's effectiveness in caption evaluation by measuring the
correlation between human judgments and metric scores.

本论文提出了一种名为 TIGEr 的新度量标准，用于自动评估图像标题生成系统，该度量标准不仅基于标题对图像内容的表现，还基于机器生成的标题与人生成的标题匹配程度；经实验测试，TIGEr 与人类判断的一致性要比其他度量标准更高，同时也通过人员评分与度量分数的相关性全面评估了该度量标准的有效性。