GROOViST：一种用于视觉叙事中物体视觉锚定评估的度量指标
GROOViST: A Metric for Grounding Objects in Visual Storytelling
AI总结：
本文提出视觉叙事评估度量 GROOViST，通过建模跨模态依赖、时间错位和人类直觉来更准确地衡量故事对图像实体的锚定程度，并支持模块化分析。
摘要：
对为图像序列生成的故事——通常称为视觉叙事的任务——进行恰当评估，必须考虑多个方面，例如连贯性、语法正确性和视觉锚定。在本工作中，我们专注于评估锚定程度，即故事在多大程度上围绕图像中出现的实体展开。我们分析了现有度量指标，包括专门为此目的设计的指标以及用于一般视觉-文本对齐的指标。鉴于它们存在的缺陷，我们提出了一种新颖的评估工具 GROOViST，该工具考虑了跨模态依赖关系、时间错位（即实体在故事中出现的顺序与图像序列中的顺序可能不匹配的事实）以及人类对视觉锚定的直觉。GROOViST 的另一个优势在于其模块化设计，每个组件的贡献都可以被单独评估和解释。
英文摘要：
A proper evaluation of stories generated for a sequence of images -- the task commonly referred to as visual storytelling -- must consider multiple aspects, such as coherence, grammatical correctness, and visual grounding. In this work, we focus on evaluating the degree of grounding, that is, the extent to which a story is about the entities shown in the images. We analyze current metrics, both designed for this purpose and for general vision-text alignment. Given their observed shortcomings, we propose a novel evaluation tool, GROOViST, that accounts for cross-modal dependencies, temporal misalignments (the fact that the order in which entities appear in the story and the image sequence may not match), and human intuitions on visual grounding. An additional advantage of GROOViST is its modular design, where the contribution of each component can be assessed and interpreted individually.
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。
