Abstract
We study personalized figure caption generation using author profile data from scientific papers. Our experiments demonstrate that rich author profile data, combined with relevant metadata, can significantly improve the personalization performance of multimodal large language models. However, we also reveal a fundamental trade-off between matching author style and maintaining caption quality. Our findings offer valuable insights and future directions for developing practical caption automation systems that balance both objectives. This work was conducted as part of the 3rd SciCap challenge.
Abstract (translated)
我们研究了利用科学论文中的作者个人资料数据进行个性化图像描述生成的方法。我们的实验表明，丰富的作者个人资料数据与相关元数据相结合，可以显著提高多模态大型语言模型的个性化性能。然而，我们也揭示了在匹配作者风格和保持描述质量之间存在一个基本的权衡。我们的研究发现为开发既能平衡这两个目标又能实际应用的图像描述自动化系统提供了宝贵的见解和未来发展方向。这项工作是作为第三届SciCap挑战赛的一部分完成的。
URL
https://arxiv.org/abs/2509.25817
