1. Faster R-CNN – Concepts
Student Presentation by:
Assaf Livne
Based on the work of:
Ross Girdhick, Shaoqing Ren, Kaiming He
אוניברסיטת בן-גוריון בנגב
Ben-Gurion University of the Negev
Faculty of Engineering Sciences
Department of Electrical Engineering

2. Introduction R-CNN Concepts Fast R-CNN Concepts Faster R-CNN Concepts
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Introduction
R-CNN Concepts
Fast R-CNN Concepts
Faster R-CNN Concepts
Conclusion

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Introduction

4. Introduction ImageNet 17/11/2016

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Introduction
Kitti

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R-CNN

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R-CNN concepts
Lets combine Localization NN and classification NN in the simplest way.

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10. Training Process Take a pre-trained classification network.
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Training Process
Take a pre-trained classification network.
Re-train the last fully connected layer with the objects that need to be detected + "no-object" class.
Get all proposals(=~2000 p/image), resize them to match the cnn input, then save to disk.
Train SVM to classify between object and background.
BB Regression: Train a linear regression classifier that will output some correction factor.

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R-CNN drawbacks
Numerous Candidate object locations must be processed – slow training time and test time
Ad hoc training objective. correlation between image space location and detected class is developing.

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Fast R-CNN

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Fast R-CNN concepts
Instead of running the CNN on every proposal lets try to save some resources.
Train all the layers in a single stage.
No memory consumption.
Inspired from the VGG16 concept - Very Deep CNN.

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ROI pooling
Type of max-pooling with a pool size dependent on the input, so that the output always has the same size.
This is done because fully connected layer always expected the same input size.

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20. R-CNN vs Fast R-CNN Testing time: mAP (VOC 2007): 49s 2.32s 66% 66.9%
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R-CNN vs Fast R-CNN
Testing time:
49s s
mAP (VOC 2007):
66% %

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Fast R-CNN drawbacks
Still depends on an external object proposal system . Which is the major bottleneck from computing resources point of view.

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Faster R-CNN

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Faster R-CNN concepts
Using the already running CNN to infer region proposals.

24. Faster R-CNN Pipe-Lines
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Faster R-CNN Pipe-Lines
Get feature maps from the deep convolution layers.
Train a Region Proposal Network (RPN).
Give proposals to the ROI pooling layer.
Send proposals to a fully connected layer to finish the classification.

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26. Region proposal Network (RPN)
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Region proposal Network (RPN)
Basically the RPN is a sliding window which slides on the feature map.
Sends as an output the locations of the proposals windows.

27. Fast R-CNN vs Faster R-CNN
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Fast R-CNN vs Faster R-CNN
Testing time:
2.32s s
mAP (VOC 2007):
66.9% %

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Conclusion

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Conclusion
“Using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN module tells the Fast R-CNN module where to look.”

30. ’Attention’ Mechanisms
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’Attention’ Mechanisms
Rather than using all available information, we need to select the most pertinent piece of information.

31. RNN – Encoder Decoder Model
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RNN – Encoder Decoder Model

32. RNN - Attention Model 17/11/2016

33. Natural Language Processing (NLP)
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Natural Language Processing (NLP)
Show, Attend and Tell – Kelvin Xu et al 2015

34. Natural Language Processing (NLP)
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Natural Language Processing (NLP)
Show, Attend and Tell – Kelvin Xu et al 2015

35. Thank you for your attention!
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Thank you for your attention!