3 papers
cs.CL2025
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…
cs.CL2025
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…
cs.CL2024
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…