YOLO-World: Real-Time Open-Vocabulary Object Detection
Open accessAbstract
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the finetuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation. Code and models are available at: https://github.com/AILab-eve/YOLO-World.
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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.”
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The introduction (‘scenarios,’ page not provided) discusses limited training data and vocabulary diversity for open-vocabulary object detection, not document tagging. Searches for document tagging and semantically tagged documents found no passage assessing that task or attributing poor performance to a scarcity of tagged documents. (LLM, conf=0.75)
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Influential
- Authors
- Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., & Shan, Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Tencent (China) (CN)
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 16901-16911
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4402716047
- Cited by
- 815
- References
- 20
- Citations by year
- 2026: 340 · 2025: 386 · 2024: 89
- FWCI
- 168.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (NSFC) (62276108)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- open-vocabulary object detection, YOLO, vision-language models, zero-shot detection, real-time detection, instance segmentation
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