Non-maximum Suppression for Object Detection by Passing Messages Between Windows
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Greedy selection does not suppress false positives.
“Finally, it does not suppress false positives [116].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [116]
BB aggregation is another group of techniques for NMS with the idea of combining or clustering multiple overlapped bounding boxes into one final detection.
“BB aggregation is another group of techniques for NMS [10, 65, 116, 117] with the idea of combining or clustering multiple overlapped bounding boxes into one final detection.”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [116]
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Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689 · see citing works
“Greedy selection is an old fashioned but the most popular way to perform NMS in object detection.”
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“For example, [26] first computes ov rlap betwee each pair of det ction boxe .”
BackgroundMethodAcquisition of Localization Confidence for Accurate Object Detection
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A survey and performance evaluation of deep learning methods for small object detection
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Improved non-maximum suppression for object detection using harmony search algorithm
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Spatial Semantic Regularisation for Large Scale Object Detection
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- Authors
- Rothe, R., Guillaumin, M., & Van Gool, L.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Rothe, Rasmus
- ORCID
- Rothe, Rasmus
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 290-306
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2126096326
- Cited by
- 218
- References
- 46
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 13 · 2023: 27 · 2022: 30 · 2021: 32 · 2020: 28 · 2019: 38 · 2018: 12 · 2017: 12 · 2016: 7 · 2015: 4
- FWCI
- 4.78
- Type
- conference paper
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- non-maximum suppression, object detection, affinity propagation, clustering, message passing, overlapping object detection
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