DEJIMA：一种新的大规模日语数据集用于图像描述和视觉问答
DEJIMA: A Novel Large-scale Japanese Dataset for Image Captioning and Visual Question Answering
AI总结：
DEJIMA是首个大规模日语图像描述与视觉问答数据集，通过整合网络收集、目标检测和大语言模型优化，提升了日语语言和文化表现力，显著优于现有翻译或人工标注数据集。
摘要：
本工作针对日本视觉与语言（V&L）建模中高质量、大规模资源匮乏的问题。我们提出了一种可扩展且可重复的流程，整合了大规模网络收集与严格的过滤/去重、基于目标检测的证据提取以及基于大语言模型（LLM）的细化，在接地约束下进行。使用该流程，我们构建了两个资源：一个图像-描述数据集（DEJIMA-Cap）和一个VQA数据集（DEJIMA-VQA），每个都包含388万个图像-文本对，远超现有日语V&L数据集的规模。人类评估表明，DEJIMA在日语性和语言自然性方面显著优于通过翻译或人工标注构建的数据集，同时在事实正确性方面与人工标注语料库相当。对图像特征分布的定量分析进一步证实，DEJIMA广泛覆盖了具有日本特色的多种视觉领域，补充了其语言和文化代表性。在多个日本多模态基准测试中，训练于DEJIMA的模型表现出一致的改进，证实了文化扎根、大规模资源在提升模型性能中的关键作用。我们流程中的所有数据源和模块均获得商业使用的许可，并公开发布由此产生的数据集和元数据，以鼓励进一步研究和工业应用。
英文摘要：
This work addresses the scarcity of high-quality, large-scale resources for Japanese Vision-and-Language (V&L) modeling. We present a scalable and reproducible pipeline that integrates large-scale web collection with rigorous filtering/deduplication, object-detection-driven evidence extraction, and Large Language Model (LLM)-based refinement under grounding constraints. Using this pipeline, we build two resources: an image-caption dataset (DEJIMA-Cap) and a VQA dataset (DEJIMA-VQA), each containing 3.88M image-text pairs, far exceeding the size of existing Japanese V&L datasets. Human evaluations demonstrate that DEJIMA achieves substantially higher Japaneseness and linguistic naturalness than datasets constructed via translation or manual annotation, while maintaining factual correctness at a level comparable to human-annotated corpora. Quantitative analyses of image feature distributions further confirm that DEJIMA broadly covers diverse visual domains characteristic of Japan, complementing its linguistic and cultural representativeness. Models trained on DEJIMA exhibit consistent improvements across multiple Japanese multimodal benchmarks, confirming that culturally grounded, large-scale resources play a key role in enhancing model performance. All data sources and modules in our pipeline are licensed for commercial use, and we publicly release the resulting dataset and metadata to encourage further research and industrial applications in Japanese V&L modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
