Papers by Jeonghun Baek
MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding (2026.findings-eacl)
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Jeonghun Baek, Kazuki Egashira, Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Hikaru Ikuta, Kiyoharu Aizawa
| Challenge: | Manga is a richly multimodal narrative form that blends images and text in complex ways. |
| Approach: | They propose two benchmarks for multimodal manga understanding: mangaOCR and mangaVQA . mangaVQ consists of 526 high-quality, manually constructed question-answer pairs . |
| Outcome: | The proposed model is finetuned from the open-source LMM Qwen2.5-VL . it compares with proprietary models such as GPT-4o and Gemini 2.5 to evaluate its performance . |
Harnessing PDF Data for Improving Japanese Large Multimodal Models (2025.findings-acl)
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| Challenge: | Large Multimodal Models (LMMs) have demonstrated strong performance in English, but their effectiveness in Japanese remains limited due to the lack of high-quality training data. |
| Approach: | They propose a pipeline that leverages pretrained models to extract image-text pairs from PDFs . they use layout analysis, OCR, and vision-language pairing to enrich the training data . |
| Outcome: | The proposed pipeline extracts image-text pairs from Japanese PDFs, eliminating manual annotations. |
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation (2025.naacl-long)
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Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, Kiyoharu Aizawa
| Challenge: | Using culture-agnostic subsets, performance drops in many LMMs when evaluated in Japanese. |
| Approach: | They introduce a Japanese benchmark to evaluate large multimodal models on expert-level tasks based on the Japanese cultural context. |
| Outcome: | The proposed benchmark enables comparisons with other benchmarks in other languages based on cultural contexts. |