DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset

• URL: http://arxiv.org/abs/2601.10305v1
• Date: Thu, 15 Jan 2026 11:28:58 GMT
• Title: DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
• Authors: Hengyu Shen, Tiancheng Gu, Bin Qin, Lan Wu, Yuling Wu, Shuo Tan, Zelong Sun, Jun Wang, Nan Wu, Xiang An, Weidong Cai, Ziyong Feng, Kaicheng Yang,
• Abstract summary: We propose DanQing, which contains 100 million image-text pairs collected from Common Crawl.<n>DanQing is curated through a more rigorous selection process, yielding superior data quality.<n>We compare DanQing with existing datasets by continual pre-training of the SigLIP2 model.
• Score: 22.47012356405577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Pre-training (VLP) models demonstrate strong performance across various downstream tasks by learning from large-scale image-text pairs through contrastive pretraining. The release of extensive English image-text datasets (e.g., COYO-700M and LAION-400M) has enabled widespread adoption of models such as CLIP and SigLIP in tasks including cross-modal retrieval and image captioning. However, the advancement of Chinese vision-language pretraining has substantially lagged behind, due to the scarcity of high-quality Chinese image-text data. To address this gap, we develop a comprehensive pipeline for constructing a high-quality Chinese cross-modal dataset. As a result, we propose DanQing, which contains 100 million image-text pairs collected from Common Crawl. Different from existing datasets, DanQing is curated through a more rigorous selection process, yielding superior data quality. Moreover, DanQing is primarily built from 2024-2025 web data, enabling models to better capture evolving semantic trends and thus offering greater practical utility. We compare DanQing with existing datasets by continual pre-training of the SigLIP2 model. Experimental results show that DanQing consistently achieves superior performance across a range of Chinese downstream tasks, including zero-shot classification, cross-modal retrieval, and LMM-based evaluations. To facilitate further research in Chinese vision-language pre-training, we will open-source the DanQing dataset under the Creative Common CC-BY 4.0 license.

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