6 papers
Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding
Jeonghun Baek, Atsuyuki Miyai, Shota Onohara +2
Manga is a culturally distinctive multimodal medium and one of the most influential forms of Japanese popular culture. As AI systems increasingly target manga understanding, OCR, a…
Beyond Bilingual Transfer: Multilingual Code-Switching in Instruction Tuning
Shunta Asano, Jeonghun Baek, Toshihiko Yamasaki
Recent studies have shown that code-switching data (CSD), in which multiple languages are mixed within the same context, can improve cross-lingual transfer and multilingual alignme…
MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
Jeonghun Baek, Kazuki Egashira, Shota Onohara +4
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…
JMMMU-Pro: Image-based Japanese Multi-discipline Multimodal Understanding Benchmark via Vibe Benchmark Construction
Atsuyuki Miyai, Shota Onohara, Jeonghun Baek +1
This paper introduces JMMMU-Pro, an image-based Japanese Multi-discipline Multimodal Understanding Benchmark, and Vibe Benchmark Construction, a scalable construction method. Follo…
Harnessing PDF Data for Improving Japanese Large Multimodal Models
Jeonghun Baek, Akiko Aizawa, Kiyoharu Aizawa
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. C…
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
Shota Onohara, Atsuyuki Miyai, Yuki Imajuku +5
Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce J…