Most existing image captioning evaluation metrics focus on assigning a single
numerical score to a caption by comparing it with reference captions. However,
these methods do not provide an explanation for the assigned score. Moreover,
reference captions are expensive to acquire. In this paper, we propose FLEUR,
an explainable reference-free metric to introduce explainability into image
captioning evaluation metrics. By leveraging a large multimodal model, FLEUR
can evaluate the caption against the image without the need for reference
captions, and provide the explanation for the assigned score. We introduce
score smoothing to align as closely as possible with human judgment and to be
robust to user-defined grading criteria. FLEUR achieves high correlations with
human judgment across various image captioning evaluation benchmarks and
reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S
within the domain of reference-free evaluation metrics. Our source code and
results are publicly available at: this https URL

借助大型多模型，本文提出了一种解释性的无参考度量方法 FLEUR，用以评估图像说明。FLEUR 不需参考说明即能对图像说明进行评估，并提供评分的解释。通过引入评分平滑化以尽可能与人类判断接近，并对用户定义的等级标准具有鲁棒性，FLEUR 在各种图像说明评估基准上与人类判断之间达到了高度相关性，并在 Flickr8k-CF、COMPOSITE 和 Pascal-50S 上取得了最先进的结果，成为了无参考评估度量领域的最新成果。