VCRScore：基于V&L Transformers、CLIP和精确率-召回率的图像描述度量指标
VCRScore: Image captioning metric based on V\&L Transformers, CLIP, and precision-recall
AI总结：
针对图像描述任务评估指标长期停滞的问题，本文提出基于V&L Transformers、CLIP和精确率-召回率的新评估指标VCRScore，构建人工标注数据集作为真实标签，实验证明其性能优于现有经典及新指标。
摘要：
图像描述已成为一项重要的视觉与语言研究任务。其核心在于为给定的特定图像或视频预测最准确的描述。研究界通过不断提出新模型和方法以提升模型整体性能，取得了令人瞩目的成果。然而，尽管相关提案不断增加，多年来用于衡量这些进展的性能度量指标却几乎没有改变。一个例证是，如今BLEU、METEOR、CIDEr和ROUGE等度量指标依然被广泛使用，此外还有BertScore和ClipScore等更复杂的度量指标。因此，有必要调整我们衡量新图像描述提案的进展、局限性和范围的方式，并使新的度量指标适应这些先进的方法。本文为图像描述问题提出了一种新的评估度量指标。为此，首先生成了一个人工标注的数据集，以评估描述与图像内容的关联程度。以这些人工评分作为真实标签，我们提出了一种新度量指标，并将其与从经典到较新的多种知名度量指标进行了比较。结果发现其取得了更优的性能，并对相关发现与见解进行了讨论。
英文摘要：
Image captioning has become an essential Vision & Language research task. It is about predicting the most accurate caption given a specific image or video. The research community has achieved impressive results by continuously proposing new models and approaches to improve the overall model's performance. Nevertheless, despite increasing proposals, the performance metrics used to measure their advances have remained practically untouched through the years. A probe of that, nowadays metrics like BLEU, METEOR, CIDEr, and ROUGE are still very used, aside from more sophisticated metrics such as BertScore and ClipScore. Hence, it is essential to adjust how are measure the advances, limitations, and scopes of the new image captioning proposals, as well as to adapt new metrics to these new advanced image captioning approaches. This work proposes a new evaluation metric for the image captioning problem. To do that, first, it was generated a human-labeled dataset to assess to which degree the captions correlate with the image's content. Taking these human scores as ground truth, we propose a new metric, and compare it with several well-known metrics, from classical to newer ones. Outperformed results were also found, and interesting insights were presented and discussed.
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。
