mlsanityData to 7 Oct 2026

MangaUB: A Manga Understanding Benchmark for Large Multimodal Models

arXiv 2407.19034 ↗First posted 26 Jul 2024cs.CV cs.MMcited by 7

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Manga is a popular medium that combines stylized drawings and text to convey stories. As manga panels differ from natural images, computational systems traditionally had to be designed specifically for manga. Recently, the adaptive nature of modern large multimodal models (LMMs) shows possibilities for more general approaches. To provide an analysis of the current capability of LMMs for manga understanding tasks and identifying areas for their improvement, we design and evaluate MangaUB, a novel manga understanding benchmark for LMMs. MangaUB is designed to assess the recognition and understanding of content shown in a single panel as well as conveyed across multiple panels, allowing for a fine-grained analysis of a model's various capabilities required for manga understanding. Our results show strong performance on the recognition of image content, while understanding the emotion and information conveyed across multiple panels is still challenging, highlighting future work towards LMMs for manga understanding.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 15 of its 23 references that are in the map. First posted on arXiv 26 Jul 2024.

Builds on

15 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Improved Baselines with Visual Instruction Tuning5 Oct
  2. 2023MMBench: Is Your Multi-modal Model an All-around Player?12 Jul

Other references · 13

  1. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  2. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
  3. 2023CogVLM: Visual Expert for Pretrained Language Modelscited by 704
  4. 2015Sketch-based Manga Retrieval using Manga109 Datasetcited by 326
  5. 2024Evolutionary Optimization of Model Merging Recipescited by 204
  6. 2024ScreenAI: A Vision-Language Model for UI and Infographics Understandingcited by 97
  7. 2016The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narrativescited by 41
  8. 2020Building a Manga Dataset "Manga109" with Annotations for Multimedia Applicationscited by 37
  9. 2020Towards Fully Automated Manga Translationcited by 21
  10. 2024The Manga Whisperer: Automatically Generating Transcriptions for Comicscited by 20
  11. 2022COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Textscited by 12
  12. 2018A survey of comics research in computer sciencecited by 9
1 more
  1. 2024Comics Datasets Framework: Mix of Comics datasets for detection benchmarkingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understandingcited by 6
  2. 2025ComicsPAP: understanding comic strips by picking the correct panelcited by 4
  3. 2025Re:Verse -- Can Your VLM Read a Manga?cited by 3
  4. 2025Zooming into Comics: Region-Aware RL Improves Fine-Grained Comic Understanding in Vision-Language Modelscited by 1
  5. 2026Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understandingcited by 1
  6. 2026Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQAcited by 0
  7. 2026Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challengescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Context-Informed Machine Translation of Manga using Multimodal Large Language Models
  2. 2025Harnessing PDF Data for Improving Japanese Large Multimodal Models
  3. 2024DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation
  4. 2024JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
  5. 2024GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models
  6. 2025CoSMo: A Multimodal Transformer for Page Stream Segmentation in Comic Books
  7. 2024LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models
  8. 2025Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review