Region Proposal

Region Proposal Network

Introduced by Ren et al. in Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like Fast R-CNN can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.

RPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.

Source: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Detection 223 21.44%
Semantic Segmentation 114 10.96%
Instance Segmentation 102 9.81%
Image Classification 19 1.83%
Classification 18 1.73%
Decoder 14 1.35%
Autonomous Driving 13 1.25%
Image Segmentation 11 1.06%
Few-Shot Object Detection 11 1.06%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories