语义相似性是漫画理解的虚假度量：来自基准实验中幻觉的教训
Semantic Similarity is a Spurious Measure of Comic Understanding: Lessons Learned from Hallucinations in a Benchmarking Experiment
AI总结：
本文提出了一项初步基准测试，评估生成视觉-语言模型在漫画解释任务中的表现，并分析了幻觉现象，强调未来研究中需加强幻觉缓解和数据整理。
摘要：
一种使盲人或视觉障碍用户能够访问漫画/漫画的系统将为这一社区引入一种新的叙事媒介。然而，目前尚无此类系统。生成视觉-语言模型（VLMs）在描述图像和理解漫画方面显示出潜力，但大多数关于漫画理解的研究局限于面板级分析。为了全面支持盲人和视觉障碍用户，必须更加关注页面级的理解和解释。在本工作中，我们提出了一个初步的VLM在漫画解释任务上的基准测试。我们识别并分类在此过程中出现的幻觉，将其组织到通用的对象幻觉分类法中。最后，我们给出未来研究的指导，强调幻觉缓解和改进漫画解释的数据整理。
英文摘要：
A system that enables blind or visually impaired users to access comics/manga would introduce a new medium of storytelling to this community. However, no such system currently exists. Generative vision-language models (VLMs) have shown promise in describing images and understanding comics, but most research on comic understanding is limited to panel-level analysis. To fully support blind and visually impaired users, greater attention must be paid to page-level understanding and interpretation. In this work, we present a preliminary benchmark of VLM performance on comic interpretation tasks. We identify and categorize hallucinations that emerge during this process, organizing them into generalized object-hallucination taxonomies. We conclude with guidance on future research, emphasizing hallucination mitigation and improved data curation for comic interpretation.
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。
