Cited by 218Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesVisual Attention and Saliency Detection

Non-maximum Suppression for Object Detection by Passing Messages Between Windows

Open access
Rothe, Rasmus, Guillaumin, Matthieu, Van Gool, Luc·2015·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/978-3-319-16865-4_19

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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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Authors
Rothe, R., Guillaumin, M., & Van Gool, L.
Affiliations
ETH Zurich (CH)
Corresponding author
Rothe, Rasmus
Venue
Lecture Notes in Computer Science
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
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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