Manga109Dialog：面向漫画说话人检测的大规模对话数据集
Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker Detection
AI总结：
针对漫画说话人检测标注不足的问题，构建了含13万余对配对的大规模数据集Manga109Dialog，并提出利用画格阅读顺序的场景图生成模型，将预测准确率提升至75%以上。
摘要：
不断扩大的电子漫画市场激发了人们对开发自动化漫画分析方法的研究兴趣。为了进一步理解漫画，需要一种自动化方法将漫画中的文本与说出这些话语的角色关联起来。漫画说话人检测研究具有实际应用价值，例如有声书的自动角色配音、根据角色性格进行自动翻译，以及推断角色关系与故事情节。为了解决说话人与文本配对标注不足的问题，我们基于Manga109创建了一个新的标注数据集Manga109Dialog。Manga109Dialog是世界上最大的漫画说话人标注数据集，包含132,692个说话人与文本配对。为了更恰当地评估说话人检测方法，我们根据预测难度将数据集进一步划分为不同级别。与主要基于距离的现有方法不同，我们提出了一种使用场景图生成模型的基于深度学习的方法。考虑到漫画的独特特征，我们通过考虑画格阅读顺序来增强所提模型的性能。我们使用Manga109Dialog及其他数据集进行了实验。实验结果表明，我们基于场景图的方法优于现有方法，预测准确率达到了75%以上。
英文摘要：
The expanding market for e-comics has spurred interest in the development of automated methods to analyze comics. For further understanding of comics, an automated approach is needed to link text in comics to characters speaking the words. Comics speaker detection research has practical applications, such as automatic character assignment for audiobooks, automatic translation according to characters' personalities, and inference of character relationships and stories. To deal with the problem of insufficient speaker-to-text annotations, we created a new annotation dataset Manga109Dialog based on Manga109. Manga109Dialog is the world's largest comics speaker annotation dataset, containing 132,692 speaker-to-text pairs. We further divided our dataset into different levels by prediction difficulties to evaluate speaker detection methods more appropriately. Unlike existing methods mainly based on distances, we propose a deep learning-based method using scene graph generation models. Due to the unique features of comics, we enhance the performance of our proposed model by considering the frame reading order. We conducted experiments using Manga109Dialog and other datasets. Experimental results demonstrate that our scene-graph-based approach outperforms existing methods, achieving a prediction accuracy of over 75%.
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。
