Methods › Computer Vision › RoI Feature Extractors › Precise RoI Pooling
Precise RoI Pooling
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.
Precise RoI Pooling, or PrRoI Pooling, is a region of interest feature extractor that avoids any quantization of coordinates and has a continuous gradient on bounding box coordinates. Given the feature map ℱ before RoI/PrRoI Pooling (eg from Conv4 in ResNet-50), let w_(i,j) be the feature at one discrete location (i,j) on the feature map. Using bilinear interpolation, the discrete feature map can be considered continuous at any continuous coordinates (x,y):
f(x,y) = ∑_(i,j)IC(x,y,i,j) ×w_(i,j),
where IC(x,y,i,j) = max(0,1-|x-i|)×max(0,1-|y-j|) is the interpolation coefficient. Then denote a bin of a RoI as bin={(x₁,y₁),(x₂,y₂)}, where (x₁,y₁) and (x₂,y₂) are the continuous coordinates of the top-left and bottom-right points, respectively. We perform pooling (e.g. average pooling) given bin and feature map ℱ by computing a two-order integral:
Papers archive 2025-07-28
4 shown of 4, 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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3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds 10 Apr 2020 · 0 repositories · arXiv:2004.04962
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Multi-Modal Fusion for End-to-End RGB-T Tracking 30 Aug 2019 · 1 repository · arXiv:1908.11714
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Hybrid Task Cascade for Instance Segmentation 22 Jan 2019 · 5 repositories · arXiv:1901.07518Syntology ran 0 of 11 samples · 11 unverified
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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
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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