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 | 224 | 21.54% |
Semantic Segmentation | 112 | 10.77% |
Instance Segmentation | 100 | 9.62% |
Image Classification | 20 | 1.92% |
Classification | 18 | 1.73% |
Decoder | 14 | 1.35% |
Autonomous Driving | 12 | 1.15% |
Image Segmentation | 11 | 1.06% |
Few-Shot Object Detection | 11 | 1.06% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |