  Popular metrics used for evaluating image captioning systems, such as BLEU
and CIDEr, provide a single score to gauge the system's overall effectiveness.
This score is often not informative enough to indicate what specific errors are
made by a given system. In this study, we present a fine-grained evaluation
method REO for automatically measuring the performance of image captioning
systems. REO assesses the quality of captions from three perspectives: 1)
Relevance to the ground truth, 2) Extraness of the content that is irrelevant
to the ground truth, and 3) Omission of the elements in the images and human
references. Experiments on three benchmark datasets demonstrate that our method
achieves a higher consistency with human judgments and provides more intuitive
evaluation results than alternative metrics.
