Publications (10)
JaWildText: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding
Koki Maeda, Naoaki Okazaki
Japanese scene text poses challenges that multilingual benchmarks often fail to capture, including mixed scripts, frequent vertical writing, and a character inventory far larger th…
JAMMEval: A Refined Collection of Japanese Benchmarks for Reliable VLM Evaluation
Issa Sugiura, Koki Maeda, Shuhei Kurita +3
Reliable evaluation is essential for the development of vision-language models (VLMs). However, Japanese VQA benchmarks have undergone far less iterative refinement than their Engl…
Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction
Koki Maeda, Shuhei Kurita, Taiki Miyanishi +1
Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closel…
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
Koki Maeda, Tosho Hirasawa, Atsushi Hashimoto +4
Procedural video understanding is gaining attention in the vision and language community. Deep learning-based video analysis requires extensive data. Consequently, existing works o…
LegalViz: Legal Text Visualization by Text To Diagram Generation
Eri Onami, Taiki Miyanishi, Koki Maeda +1
Legal documents including judgments and court orders require highly sophisticated legal knowledge for understanding. To disclose expert knowledge for non-experts, we explore the pr…
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Koshiro Saito, Sakae Mizuki, Masanari Ohi +11
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-spe…
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model
Keito Sasagawa, Koki Maeda, Issa Sugiura +3
To develop high-performing Visual Language Models (VLMs), it is essential to prepare multimodal resources, such as image-text pairs, interleaved data, and instruction data. While m…
Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models
Issa Sugiura, Keito Sasagawa, Keisuke Nakao +8
Developing vision-language models (VLMs) that generalize across diverse tasks requires large-scale training datasets with diverse content. In English, such datasets are typically c…
From Correspondence to Actions: Human-Like Multi-Image Spatial Reasoning in Multi-modal Large Language Models
Masanari Oi, Koki Maeda, Ryuto Koike +3
While multimodal large language models (MLLMs) have made substantial progress in single-image spatial reasoning, multi-image spatial reasoning, which requires integration of inform…
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Youmi Ma, Sakae Mizuki, Kazuki Fujii +12
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized sol…