title: MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
    Manga Understanding
summary: Manga, or Japanese comics, is a richly multimodal narrative form that blends
  images and text in complex ways. Teaching large multimodal models (LMMs) to
  understand such narratives at a human-like level could help manga creators
  reflect on and refine their stories. To this end, we introduce two benchmarks
  for multimodal manga understanding: MangaOCR, which targets in-page text
  recognition, and MangaVQA, a novel benchmark designed to evaluate contextual
  understanding through visual question answering. MangaVQA consists of 526
  high-quality, manually constructed question-answer pairs, enabling reliable
  evaluation across diverse narrative and visual scenarios. Building on these
  benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the
  open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive
  experiments, including comparisons with proprietary models such as GPT-4o and
  Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model
  provide a comprehensive foundation for evaluating and advancing LMMs in the
  richly narrative domain of manga.
paper_authors._id: 6838433e66c3531dee69e88b
paper_authors.name: Jeonghun Baek
paper_authors._id: 6838433e66c3531dee69e88c
paper_authors.name: Kazuki Egashira
paper_authors._id: 6838433e66c3531dee69e88d
paper_authors.name: Shota Onohara
paper_authors._id: 6838433e66c3531dee69e88e
paper_authors.name: Atsuyuki Miyai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T20:19:13.446Z
paper_authors._id: 6527b37c0ae663e384eb1b85
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6527b37c0ae663e384eb1b85/zKWa8h6YU4BWfcitpM5Pl.png
paper_authors.fullname: Atsuyuki Miyai
paper_authors.type: user
paper_authors.user: AtsuMiyai
paper_authors._id: 6838433e66c3531dee69e88f
paper_authors.name: Yuki Imajuku
paper_authors._id: 6838433e66c3531dee69e890
paper_authors.name: Hikaru Ikuta
paper_authors._id: 6838433e66c3531dee69e891
paper_authors.name: Kiyoharu Aizawa
paper_title: MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
    Manga Understanding
paper_summary: Manga, or Japanese comics, is a richly multimodal narrative form that blends
  images and text in complex ways. Teaching large multimodal models (LMMs) to
  understand such narratives at a human-like level could help manga creators
  reflect on and refine their stories. To this end, we introduce two benchmarks
  for multimodal manga understanding: MangaOCR, which targets in-page text
  recognition, and MangaVQA, a novel benchmark designed to evaluate contextual
  understanding through visual question answering. MangaVQA consists of 526
  high-quality, manually constructed question-answer pairs, enabling reliable
  evaluation across diverse narrative and visual scenarios. Building on these
  benchmarks, we develop MangaLMM, a manga-specialized model finetuned from the
  open-source LMM Qwen2.5-VL to jointly handle both tasks. Through extensive
  experiments, including comparisons with proprietary models such as GPT-4o and
  Gemini 2.5, we assess how well LMMs understand manga. Our benchmark and model
  provide a comprehensive foundation for evaluating and advancing LMMs in the
  richly narrative domain of manga.
paper_ai_summary: Two new benchmarks, MangaOCR and MangaVQA, and a specialized model, MangaLMM, are introduced to evaluate and advance large multimodal models in understanding manga narratives.
paper_ai_keywords: Multimodal models MangaOCR MangaVQA contextual understanding visual question answering MangaLMM Qwen2.5-VL GPT-4o Gemini 2.5