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