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[Review] 4. Faster R-CNN

1. Improvement from Fast R-CNN by introducing a Region Proposal Network

2. Faster R-CNN Architecture

Figure 1. Faster R-CNN architecture consists of separately trained and merged RPN and Fast R-CNN, from [2]

3. Region Proposal Networks (RPN)

Figure 2. RPN producing objectness predictions at a given grid point for k anchor boxes.

3.1 Does RPN actually avoids enumerating filters of multiple scales or aspect ratios?

Figure 3. Different schemes for addressing multiple scales and sizes. (a) Pyramids of images and feature maps are built, and the classifier is run at all scales. (b) Pyramids of filters with multiple scales/sizes are run on the feature map. © pyramids of reference boxes in the regression functions, from [1]

4. Training RPN

5. ROI warping

Figure 4. Illustration of why quantization and RoI pooling does not work for RPN backpropagation, from [6]

6. Training Faster R-CNN

Figure 5. 4 training steps of Faster R-CNN, from [3]

Summary

Figure 6. Difference between R-CNN series, from [4]

Reference

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This publication is for organizing and posting what I’ve studied in scope of CS and Mathematics

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