WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models

• URL: http://arxiv.org/abs/2510.22276v1
• Date: Sat, 25 Oct 2025 12:42:42 GMT
• Title: WAON: Large-Scale and High-Quality Japanese Image-Text Pair Dataset for Vision-Language Models
• Authors: Issa Sugiura, Shuhei Kurita, Yusuke Oda, Daisuke Kawahara, Yasuo Okabe, Naoaki Okazaki,
• Abstract summary: WAON is a large-scale and high-quality Japanese image-text pair dataset.<n>To evaluate its effectiveness, we construct WAON-Bench, a benchmark for Japanese cultural image classification.<n>We fine-tune SigLIP2, a strong multilingual model, on both datasets.
• Score: 29.864478753087138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale and high-quality image-text pair datasets play an important role in developing high-performing Vision-Language Models (VLMs). In this work, we introduce WAON, a large-scale and high-quality Japanese image-text pair dataset containing approximately 155 million examples, collected from Common Crawl. Our dataset construction pipeline employs various techniques, including filtering and deduplication, which have been shown to be effective in previous studies. To evaluate its effectiveness, we also construct WAON-Bench, a manually curated benchmark for Japanese cultural image classification, consisting of 374 classes. To assess the effectiveness of our dataset, we conduct experiments using both WAON and the Japanese subset of ReLAION, one of the most widely used vision-language datasets. We fine-tune SigLIP2, a strong multilingual model, on both datasets. The results demonstrate that WAON enhances model performance on WAON-Bench more efficiently than ReLAION and achieves higher accuracy across all evaluated benchmarks. Furthermore, the model fine-tuned on WAON achieves state-of-the-art performance on several Japanese cultural benchmarks. We release our dataset, model, and code at https://speed1313.github.io/WAON.

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