mlsanityData to 14 Oct 2026

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

arXiv 2601.10305 ↗First posted 15 Jan 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

Vision-Language Pre-training (VLP) models have achieved remarkable success by leveraging large-scale image-text pairs. While English-centric models like CLIP and SigLIP benefit from massive datasets (e.g., LAION-400M), the development of Chinese VLP remains bottlenecked by the lack of high-quality, large-scale open-source data. In this paper, we present DanQing, a large-scale Chinese cross-modal dataset containing 100 million high-quality image-text pairs curated from Common Crawl. To ensure superior data quality, we develop an effective systematic pipeline comprising data source selection, text refinement, visual diversification, and cross-modal cross-batch filtering, thereby effectively mitigating the intrinsic noise prevalent in web data. Notably, DanQing incorporates data from 2024-2025, enabling models to capture contemporary semantic trends and emerging concepts. Extensive experiments via continued pretraining of SigLIP2 models demonstrate that DanQing consistently outperforms existing Chinese datasets across diverse downstream tasks, including zero-shot classification, cross-modal retrieval, and Chinese-centric large multimodal model tasks. Furthermore, in-depth analysis of DanQing reveals that it exhibits a more balanced semantic distribution and superior scaling capability compared to existing datasets. To facilitate further research in Chinese vision-language pre-training, we will open-source the DanQing dataset under the Creative Common CC-BY-NC 4.0 license.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 57 of its 80 references that are in the map. First posted on arXiv 15 Jan 2026.

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57 of 80 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022CCMB: A Large-scale Chinese Cross-modal Benchmark8 May
  2. 2022WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models22 Mar
  3. 2022Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark14 Feb
  4. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation28 Jan

Other references · 53

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  5. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
  6. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  7. 2017Billion-scale similarity search with GPUscited by 2,497
  8. 2023MMBench: Is Your Multi-modal Model an All-around Player?cited by 2,141
  9. 2013Describing Textures in the Wildcited by 2,125
  10. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,555
  11. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,274
  12. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,216
38 more
  1. 2025SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Featurescited by 1,188
  2. 2021Grounded Language-Image Pre-trainingcited by 1,156
  3. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  4. 2017Remote Sensing Image Scene Classification: Benchmark and State of the Artcited by 797
  5. 2022OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Frameworkcited by 745
  6. 2022Language-driven Semantic Segmentationcited by 717
  7. 2023DataComp: In search of the next generation of multimodal datasetscited by 644
  8. 2021RegionCLIP: Region-based Language-Image Pretrainingcited by 616
  9. 2021ClipCap: CLIP Prefix for Image Captioningcited by 559
  10. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 532
  11. 2022GroupViT: Semantic Segmentation Emerges from Text Supervisioncited by 531
  12. 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 490
  13. 2021SLIP: Self-supervision meets Language-Image Pre-trainingcited by 436
  14. 2024Long-CLIP: Unlocking the Long-Text Capability of CLIPcited by 300
  15. 2024MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?cited by 292
  16. 2021WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learningcited by 287
  17. 2023Demystifying CLIP Datacited by 270
  18. 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 266
  19. 2023Data Filtering Networkscited by 250
  20. 2021RedCaps: web-curated image-text data created by the people, for the peoplecited by 180
  21. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 173
  22. 2024DOCCI: Descriptions of Connected and Contrasting Imagescited by 132
  23. 2022Chinese CLIP: Contrastive Vision-Language Pretraining in Chinesecited by 109
  24. 2023A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captionscited by 108
  25. 2018COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrievalcited by 80
  26. 2025Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMscited by 73
  27. 2023ALIP: Adaptive Language-Image Pre-training with Synthetic Captioncited by 70
  28. 2025Meta CLIP 2: A Worldwide Scaling Recipecited by 64
  29. 2024CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmarkcited by 56
  30. 2021Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretrainingcited by 42
  31. 2025UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learningcited by 42
  32. 2017Fluency-Guided Cross-Lingual Image Captioningcited by 33
  33. 2024RWKV-CLIP: A Robust Vision-Language Representation Learnercited by 23
  34. 2024CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discriminationcited by 21
  35. 2025FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Modelcited by 13
  36. 2025When and How Does CLIP Enable Domain and Compositional Generalization?cited by 9
  37. 2025Decoupled Global-Local Alignment for Improving Compositional Understandingcited by 9
  38. 2023Grounded Image Text Matching with Mismatched Relation Reasoningcited by 8

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