TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Methods/Precise RoI Pooling

Precise RoI Pooling

Computer VisionIntroduced 20004 papers
Source Paper

Description

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\mathcal{F}F before RoI/PrRoI Pooling (eg from Conv4 in ResNet-50), let wi,jw_{i,j}wi,j​ be the feature at one discrete location (i,j)(i,j)(i,j) on the feature map. Using bilinear interpolation, the discrete feature map can be considered continuous at any continuous coordinates (x,y)(x,y)(x,y):

f(x,y)=∑i,jIC(x,y,i,j)×wi,j,f(x,y) = \sum_{i,j}IC(x,y,i,j) \times w_{i,j},f(x,y)=i,j∑​IC(x,y,i,j)×wi,j​,

where IC(x,y,i,j)=max(0,1−∣x−i∣)×max(0,1−∣y−j∣)IC(x,y,i,j) = max(0,1-|x-i|)\times max(0,1-|y-j|)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)}bin=\{(x_1,y_1),(x_2,y_2)\}bin={(x1​,y1​),(x2​,y2​)}, where (x1,y1)(x_1,y_1)(x1​,y1​) and (x2,y2)(x_2,y_2)(x2​,y2​) are the continuous coordinates of the top-left and bottom-right points, respectively. We perform pooling (e.g. average pooling) given binbinbin and feature map F\mathcal{F}F by computing a two-order integral:

Papers Using This Method

3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds2020-04-10Multi-Modal Fusion for End-to-End RGB-T Tracking2019-08-30Hybrid Task Cascade for Instance Segmentation2019-01-22Acquisition of Localization Confidence for Accurate Object Detection2018-07-30