Methods › Computer Vision › Proposal Filtering › Matrix NMS
Matrix Non-Maximum Suppression
Matrix NMS
Introduced by Xinlong Wang et al. in SOLOv2: Dynamic and Fast Instance Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Matrix NMS, or Matrix Non-Maximum Suppression, performs non-maximum suppression with parallel matrix operations in one shot. It is motivated by Soft-NMS. Soft-NMS decays the other detection scores as a monotonic decreasing function f(iou) of their overlaps. By decaying the scores according to IoUs recursively, higher IoU detections will be eliminated with a minimum score threshold. However, such process is sequential like traditional Greedy NMS and can not be implemented in parallel.
Matrix NMS views this process from another perspective by considering how a predicted mask mⱼ being suppressed. For mⱼ, its decay factor is affected by: (a) The penalty of each prediction mᵢ on mⱼ (sᵢ>sⱼ), where sᵢ and sⱼ are the confidence scores; and (b) the probability of mᵢ being suppressed. For (a), the penalty of each prediction mᵢ on mⱼ could be easily computed by f(. iou ._(i, j)). For (b), the probability of mᵢ being suppressed is not so elegant to be computed. However, the probability usually has positive correlation with the IoUs. So here we directly approximate the probability by the most overlapped prediction on mᵢ as
f( iou. _(, i))=min_(∀sₖ>sᵢ) f( iou _(k, i))
To this end, the final decay factor becomes
decayⱼ=min_(∀sᵢ>sⱼ) (f( iou _(i, j)))/(f( iou _(·, i)))
and the updated score is computed by sⱼ=sⱼ · decay ⱼ . The authors consider the two most simple decremented functions, denoted as linear f(. iou ._(i, j))=1- iou _(i, j), and Gaussian f(. iou ._(i, j))=exp(-(i o u_(i, j)²)/σ).
Papers archive 2025-07-28
5 shown of 5, 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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PP-YOLOE: An evolved version of YOLO 30 Mar 2022 · 8 repositories · arXiv:2203.16250Syntology ran 5 of 27 samples · 22 unverified
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In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos 27 Jan 2022 · 0 repositories · arXiv:2201.11450
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PP-YOLOv2: A Practical Object Detector 21 Apr 2021 · 1 repository · arXiv:2104.10419
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PP-YOLO: An Effective and Efficient Implementation of Object Detector 23 Jul 2020 · 5 repositories · arXiv:2007.12099
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SOLOv2: Dynamic and Fast Instance Segmentation 23 Mar 2020 · 18 repositories · arXiv:2003.10152Syntology ran 15 of 38 samples · 23 unverified · 24 pointer-only (licence)
Tasks archive 2025-07-28
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