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 NetworksPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Object Detection | 231 | 22.51% |
Semantic Segmentation | 122 | 11.89% |
Instance Segmentation | 110 | 10.72% |
Image Classification | 21 | 2.05% |
Autonomous Driving | 19 | 1.85% |
Classification | 18 | 1.75% |
General Classification | 12 | 1.17% |
Few-Shot Object Detection | 11 | 1.07% |
Domain Adaptation | 11 | 1.07% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |