Despite the success of various text generation metrics such as BERTScore, it
is still difficult to evaluate the image captions without enough reference
captions due to the diversity of the descriptions. In this paper, we introduce
a new metric UMIC, an Unreferenced Metric for Image Captioning which does not
require reference captions to evaluate image captions. Based on
Vision-and-Language BERT, we train UMIC to discriminate negative captions via
contrastive learning. Also, we observe critical problems of the previous
benchmark dataset (i.e., human annotations) on image captioning metric, and
introduce a new collection of human annotations on the generated captions. We
validate UMIC on four datasets, including our new dataset, and show that UMIC
has a higher correlation than all previous metrics that require multiple
references. We release the benchmark dataset and pre-trained models to compute
the UMIC.

本文提出了一种新的评估图像字幕的度量 UMIC，其通过对比学习不需要参考字幕。同时，我们发现以前的标准数据集在图像字幕度量方面存在关键问题，并对生成字幕进行了新的人类注释。在包括我们的新数据集在内的四个数据集上验证了 UMIC，证明了 UMIC 的相关性比所有需要多个参考文本度量的先前度量更高。我们发布了基准数据集和预训练模型以计算 UMIC。