An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics

TL;DR

This paper critically evaluates the robustness of reference-free image captioning metrics like CLIPScore, UMIC, and PAC-S, revealing their limitations in detecting fine-grained errors and understanding linguistic nuances.

Contribution

It provides a comprehensive analysis of the weaknesses of current reference-free metrics, highlighting areas for future improvement in image caption evaluation.

Findings

Metrics struggle with fine-grained error detection.

Sensitivity to visual grounding errors is high.

Weak understanding of negation and caption structure.

Abstract

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.