  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: https://github.com/Yebin46/FLEUR.
