RepPoints: Point Set Representation for Object Detection
Open accessAbstract
Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present RepPoints (representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 AP50 on the COCO test-dev detection benchmark, using ResNet-101 model. Code is available at https://github.com/microsoft/RepPoints.
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Some other methods utilize more key points (corner and center, extreme and center points, representative points) to obtain better performance.
“Some other methods follow the idea and utilize more key points (corner and center [77], extreme and center points [53], representative points [69] ) to obtain better performance.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [69]
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Background
- Authors
- Yang, Z., Liu, S., Hu, H., Wang, L., & Lin, S.
- Affiliations
- Peking University (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9656-9665
- OpenAlex
- W2986357608
- Cited by
- 1,307
- References
- 51
- Citations by year
- 2026: 85 · 2025: 207 · 2024: 270 · 2023: 263 · 2022: 202 · 2021: 182 · 2020: 88 · 2019: 10
- FWCI
- 39.31
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- point set representation, anchor-free detection, object localization, object recognition, COCO benchmark, ResNet-101
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