  Given the accelerating progress of vision and language modeling, accurate
evaluation of machine-generated image captions remains critical. In order to
evaluate captions more closely to human preferences, metrics need to
discriminate between captions of varying quality and content. However,
conventional metrics fail short of comparing beyond superficial matches of
words or embedding similarities; thus, they still need improvement. This paper
presents VisCE$^2$, a vision language model-based caption evaluation method.
Our method focuses on visual context, which refers to the detailed content of
images, including objects, attributes, and relationships. By extracting and
organizing them into a structured format, we replace the human-written
references with visual contexts and help VLMs better understand the image,
enhancing evaluation performance. Through meta-evaluation on multiple datasets,
we validated that VisCE$^2$ outperforms the conventional pre-trained metrics in
capturing caption quality and demonstrates superior consistency with human
judgment.
