We establish THumB, a rubric-based human evaluation protocol for image
captioning models. Our scoring rubrics and their definitions are carefully
developed based on machine- and human-generated captions on the MSCOCO dataset.
Each caption is evaluated along two main dimensions in a tradeoff (precision
and recall) as well as other aspects that measure the text quality (fluency,
conciseness, and inclusive language). Our evaluations demonstrate several
critical problems of the current evaluation practice. Human-generated captions
show substantially higher quality than machine-generated ones, especially in
coverage of salient information (i.e., recall), while most automatic metrics
say the opposite. Our rubric-based results reveal that CLIPScore, a recent
metric that uses image features, better correlates with human judgments than
conventional text-only metrics because it is more sensitive to recall. We hope
that this work will promote a more transparent evaluation protocol for image
captioning and its automatic metrics.

本文介绍了一种基于机器和人生成的 MSCOCO 数据集上的图像标注模型的评估协议 THumB，用于评估图像文本的质量。我们的实验发现，使用图像特征的近期度量值 CLIPScore 更符合人类评判标准。