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 221 21.60%
Semantic Segmentation 116 11.34%
Instance Segmentation 104 10.17%
Image Classification 19 1.86%
Classification 18 1.76%
Autonomous Driving 13 1.27%
Image Segmentation 11 1.08%
Few-Shot Object Detection 11 1.08%
Text Classification 10 0.98%

Components


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

Categories