Recently, reference-free metrics such as CLIPScore (Hessel et al., 2021) and UMIC (Lee et al., 2021) have been proposed for automatic evaluation of image captions, demonstrating a high correlation with human judgment. In this work, 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 judgment, both CLIPScore and UMIC struggle to identify fine-grained errors in captions. However, when comparing different types of fine-grained errors, both metrics exhibit limited sensitivity to implausibility of captions and strong sensitivity to lack of sufficient visual grounding. Probing further into the visual grounding aspect, we found that both CLIPScore and UMIC are impacted by the size of image-relevant objects mentioned in the caption, and that CLIPScore is also sensitive to the number of mentions of image-relevant objects in the caption. In terms of linguistic aspects of a caption, we found that both metrics lack the ability to comprehend negation, UMIC is sensitive to caption lengths, and CLIPScore is insensitive to the structure of the sentence. We hope our findings will serve as a valuable guide towards improving reference-free evaluation in image captioning.

翻译：近期，CLIPScore（Hessel等，2021）与UMIC（Lee等，2021）等无参考指标被提出用于图像描述的自动评价，并显示出与人类判断的高度相关性。本研究聚焦于评估这些指标在区分词汇高度重叠但语义迥异的两个描述时的鲁棒性。研究发现，尽管与人类判断高度相关，CLIPScore和UMIC均难以识别描述中的细微错误。然而，在比较不同类型的细微错误时，两种指标对描述内容的合理性缺乏敏感性，而对视觉依据不足则表现出强烈敏感性。进一步探究视觉依据层面发现，两种指标均受描述中提及的图像相关对象大小影响，且CLIPScore对描述中提及该对象的次数敏感。在描述的语言特征方面，两种指标均不具备理解否定结构的能力，UMIC对描述长度敏感，而CLIPScore不受句子结构影响。我们期望这些发现能为改进图像描述的无参考评价提供有益指导。