Methods › Computer Vision › Proposal Filtering › IoU-guided NMS
IoU-guided NMS
Introduced by Borui Jiang et al. in Acquisition of Localization Confidence for Accurate Object Detection
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
IoU-guided NMS is a type of non-maximum suppression that help to eliminate the suppression failure caused by the misleading classification confidences. This is achieved through using the predicted IoU instead of the classification confidence as the ranking keyword for bounding boxes.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Acquisition of Localization Confidence for Accurate Object Detection 30 Jul 2018 · 4 repositories · arXiv:1807.11590Syntology ran 2 of 18 samples · 16 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| General Classification | 1 |
| Object | 1 |
| Object Detection | 1 |
| object-detection | 1 |
| regression | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections