Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection
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Most existing approaches perform poorly on document tagging tasks, likely due to the scarcity of semantically tagged documents in their training sets.
“In practice, however, most existing approaches [5, 6, 14] perform poorly on document tagging tasks (see Figure 2), likely due to the scarcity of semantically tagged documents in their training sets.”
From iTagPDF: Towards Finally Automating PDF Accessibility (2026) · cites this paper as [14]
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- Authors
- Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., Zhu, J., & Zhang, L.
- Affiliations
- State Key Laboratory of Intelligent Technology and Systems (CN)
- Hong Kong University of Science and Technology (HK)
- Microsoft (United States) (US)
- South China University of Technology (CN)
- Corresponding author
- Zhu, Jun
- Zhang, Lei
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024-11-23
- Pages
- 38-55
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4404612908
- Cited by
- 2,074
- References
- 13
- Citations by year
- 2026: 704 · 2025: 922 · 2024: 419 · 2023: 29
- FWCI
- 422.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- open-set object detection, DINO, cross-modal fusion, zero-shot detection, concept generalization, vision-language models
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