MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
  Manga Understanding
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.
6838433e66c3531dee69e88b
Jeonghun Baek
6838433e66c3531dee69e88c
Kazuki Egashira
6838433e66c3531dee69e88d
Shota Onohara
6838433e66c3531dee69e88e
Atsuyuki Miyai
claimed_verified
2025-05-29T20:19:13.446Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6527b37c0ae663e384eb1b85/zKWa8h6YU4BWfcitpM5Pl.png
Atsuyuki Miyai
user
AtsuMiyai
6838433e66c3531dee69e88f
Yuki Imajuku
claimed_verified
2025-07-01T10:15:17.890Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65d86a59bdb95b4bbc744e10/TUYziiCbQr8JN0LZSxoTY.jpeg
Yuki Imajuku
user
yuki-imajuku
6838433e66c3531dee69e890
Hikaru Ikuta
6838433e66c3531dee69e891
Kiyoharu Aizawa
MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal
  Manga Understanding
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.
Two new benchmarks, MangaOCR and MangaVQA, and a specialized model, MangaLMM, are introduced to evaluate and advance large multimodal models in understanding manga narratives.
Multimodal models MangaOCR MangaVQA contextual understanding visual question answering MangaLMM Qwen2.5-VL GPT-4o Gemini 2.5