  Recently, reference-free metrics such as CLIPScore (Hessel et al., 2021),
UMIC (Lee et al., 2021), and PAC-S (Sarto et al., 2023) have been proposed for
automatic reference-free evaluation of image captions. Our focus lies in
evaluating the robustness of these metrics in scenarios that require
distinguishing between two captions with high lexical overlap but very
different meanings. Our findings reveal that despite their high correlation
with human judgments, CLIPScore, UMIC, and PAC-S struggle to identify
fine-grained errors. While all metrics exhibit strong sensitivity to visual
grounding errors, their sensitivity to caption implausibility errors is
limited. Furthermore, we found that all metrics are sensitive to variations in
the size of image-relevant objects mentioned in the caption, while CLIPScore
and PAC-S are also sensitive to the number of mentions of image-relevant
objects in the caption. Regarding linguistic aspects of a caption, all metrics
show weak comprehension of negation, and CLIPScore and PAC-S are insensitive to
the structure of the caption to a great extent. We hope our findings will guide
further improvements in reference-free evaluation of image captioning.
