  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.

Computation and Language;Computer Vision and Pattern Recognition