MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
  Manga Understanding
Authors: name: Jeonghun Baek, name: Kazuki Egashira, name: Shota Onohara, name: Atsuyuki Miyai, name: Yuki Imajuku, name: Hikaru Ikuta, name: Kiyoharu Aizawa

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.