# ComicsPAP: understanding comic strips by picking the correct panel

arXiv: https://arxiv.org/abs/2503.08561 · HTML: https://mlsanity.com/paper/2503.08561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Image editing and quality assessment](https://mlsanity.com/mllm/image-editing-and-quality-assessment/)

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://mlsanity.com/paper/2409.17146/) (2024)
- [MANTIS: Interleaved Multi-Image Instruction Tuning](https://mlsanity.com/paper/2405.01483/) (2024)
- [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://mlsanity.com/paper/2412.03555/) (2024)
- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation](https://mlsanity.com/paper/2412.07589/) (2024)
- [CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding](https://mlsanity.com/paper/2407.03550/) (2024)
- [Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names](https://mlsanity.com/paper/2408.00298/) (2024)
- [MangaUB: A Manga Understanding Benchmark for Large Multimodal Models](https://mlsanity.com/paper/2407.19034/) (2024)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [Comics Datasets Framework: Mix of Comics datasets for detection benchmarking](https://mlsanity.com/paper/2407.03540/) (2024)
- [Multimodal Transformer for Comics Text-Cloze](https://mlsanity.com/paper/2403.03719/) (2024)
- [ComiCap: A VLMs pipeline for dense captioning of Comic Panels](https://mlsanity.com/paper/2409.16159/) (2024)

## Built on by (most cited first, 5 of 5)

- [Zooming into Comics: Region-Aware RL Improves Fine-Grained Comic Understanding in Vision-Language Models](https://mlsanity.com/paper/2511.06490/) (2025)
- [DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models](https://mlsanity.com/paper/2512.01686/) (2025)
- [Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges](https://mlsanity.com/paper/2607.19011/) (2026)
- [ManGo: Manga Active Narrative Grounding Optimization](https://mlsanity.com/paper/2608.29865/) (2026)
- [Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga](https://mlsanity.com/paper/2610.13840/) (2026)

## Abstract (arXiv)

Large multimodal models (LMMs) have made impressive strides in image captioning, VQA, and video comprehension, yet they still struggle with the intricate temporal and spatial cues found in comics. To address this gap, we introduce ComicsPAP, a large-scale benchmark designed for comic strip understanding. Comprising over 100k samples and organized into 5 subtasks under a Pick-a-Panel framework, ComicsPAP demands models to identify the missing panel in a sequence. Our evaluations, conducted under both multi-image and single-image protocols, reveal that current state-of-the-art LMMs perform near chance on these tasks, underscoring significant limitations in capturing sequential and contextual dependencies. To close the gap, we adapted LMMs for comic strip understanding, obtaining better results on ComicsPAP than 10x bigger models, demonstrating that ComicsPAP offers a robust resource to drive future research in multimodal comic comprehension.