EXPERT：一种具有结构化解释的可解释图像描述评估指标
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations
AI总结：
本文提出无需参考的图像描述评估指标 EXPERT，基于流畅性、相关性和描述性提供结构化解释，通过构建大规模数据集和两阶段评估模板监督视觉语言模型，在基准数据集上取得最优结果且解释质量显著提升。
摘要：
大型语言模型和视觉语言模型的最新进展，引发了人们对图像描述可解释评估指标日益增长的兴趣。然而，这些指标在生成解释时缺乏标准化准则，且生成解释的整体质量仍有待验证。本文提出了一种无需参考的评估指标 EXPERT，它基于流畅性、相关性和描述性三个基本准则提供结构化解释。通过构建高质量结构化解释的大规模数据集，我们开发了两阶段评估模板，以有效监督视觉语言模型进行评分和解释生成。EXPERT 在基准数据集上取得了最先进的结果，同时通过全面的人工评估验证，其提供的解释质量显著高于现有指标。
英文摘要：
Recent advances in large language models and vision-language models have led to growing interest in explainable evaluation metrics for image captioning. However, these metrics generate explanations without standardized criteria, and the overall quality of the generated explanations remains unverified. In this paper, we propose EXPERT, a reference-free evaluation metric that provides structured explanations based on three fundamental criteria: fluency, relevance, and descriptiveness. By constructing large-scale datasets of high-quality structured explanations, we develop a two-stage evaluation template to effectively supervise a vision-language model for both scoring and explanation generation. EXPERT achieves state-of-the-art results on benchmark datasets while providing significantly higher-quality explanations than existing metrics, as validated through comprehensive human evaluation. Our code and datasets are available at https://github.com/hjkim811/EXPERT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
