图像描述评估领域的全景调查：全面分类法、趋势与指标分析
Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis
AI总结：
本文首次对70多种图像描述评估指标进行分类调查，发现多数研究仅依赖五种与人类评分弱相关的指标，并提出基于线性回归的集成方法EnsembEval以提升评估效果。
摘要：
图像描述任务近期日益受到关注，随之而来的是评估图像描述模型质量的复杂任务。在本研究中，我们首次对超过70种不同的图像描述指标及其在数百篇论文中的应用进行了调查和分类，专门旨在帮助用户选择最适合其需求的指标。我们发现，尽管提出了多种多样的指标，但绝大多数研究仅依赖五种流行指标，而我们表明这些指标与人类评分的相关性很弱。我们假设，结合一组多样化的指标可以增强与人类评分的相关性。作为初步步骤，我们证明了一种基于线性回归的集成方法（我们称之为EnsembEval），在一个人类评分数据集上训练后，在另外五个数据集上实现了改善的相关性，这表明通过利用多样化的指标仍有很大的改进空间。
英文摘要：
The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the first survey and taxonomy of over 70 different image captioning metrics and their usage in hundreds of papers, specifically designed to help users select the most suitable metric for their needs. We find that despite the diversity of proposed metrics, the vast majority of studies rely on only five popular metrics, which we show to be weakly correlated with human ratings. We hypothesize that combining a diverse set of metrics can enhance correlation with human ratings. As an initial step, we demonstrate that a linear regression-based ensemble method, which we call EnsembEval, trained on one human ratings dataset, achieves improved correlation across five additional datasets, showing there is a lot of room for improvement by leveraging a diverse set of metrics.
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。
