Answers.Body: <p>I just want to provide this intuition </p>
  
  <ul>
  <li><p>this NN consists of a 2 steps detection pipeline (the region proposal and regression + classification in parallel) exploring a certain range of scales and aspect ratios for proposals</p></li>
  <li><p>as the proposed region is rectangular and the objects of interests have not a rectangular appearance strictly speaking, but a mostly rectangular one (with different ARs), considering the resulting detection bboxes areas some pixels will belong to a certain relevant object appearance (e.g. pixels of a pedestrian, car, …) while other pixels in that BBox won’t be related to that semantic</p></li>
  <li><p>as this is not semantic segmentation we can now know what pixel is actually relevant, however the <em>objectness</em> and <em>backgroundness</em> can provide a rough measure of this in an aggregated way</p></li>
  </ul>
  
  <p>Let’s assume at some point in your processing pipeline, you have 2 regions containing the same car but at different scales: so ideally both of them should be associated with good confidence to CarID but it’s clear one will contain much more background pixels than the other one and you'd like this to be represented as an additional measure </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-19T01:44:25.290Z
Answers.Text: That's a fantastic answer for a year-old question. Thanks!
Answers.UserDisplayName: dseuss
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-18T12:41:44.480Z
Answers.LastActivityDate: 2019-03-18T12:41:44.480Z
Answers.UserDisplayName: Nicola Bernini
Body: <p>The region proposal network (RPN) in Faster-RCNN models contains a classifier and a regressor network. Why does the classifier network output two scores (object and background) for each anchor instead of just a single probability? Aren't the two classes considered exclusive?</p>
  
  <p>Source: Figure 3 of the <a href="https://arxiv.org/abs/1506.01497" rel="nofollow noreferrer">original Faster-RCNN paper</a></p>
  
Tags: <machine-learning><convolutional-neural-networks><object-recognition>
Title: Why does the classifier network in RPN output two scores?
UserDisplayName: dseuss
ThreadText: # Why does the classifier network in RPN output two scores?
  
  (Asked by: username_0 on 2018-02-27)
  
  The region proposal network (RPN) in Faster-RCNN models contains a classifier and a regressor network. Why does the classifier network output two scores (object and background) for each anchor instead of just a single probability? Aren't the two classes considered exclusive?
  
  Source: Figure 3 of the [original Faster-RCNN paper](https://arxiv.org/abs/1506.01497)
  
  **Tags:** machine-learning, convolutional-neural-networks, object-recognition
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-18. Score: 3 | Accepted answer
  
  I just want to provide this intuition
  
  * this NN consists of a 2 steps detection pipeline (the region proposal and regression + classification in parallel) exploring a certain range of scales and aspect ratios for proposals
  * as the proposed region is rectangular and the objects of interests have not a rectangular appearance strictly speaking, but a mostly rectangular one (with different ARs), considering the resulting detection bboxes areas some pixels will belong to a certain relevant object appearance (e.g. pixels of a pedestrian, car, …) while other pixels in that BBox won’t be related to that semantic
  * as this is not semantic segmentation we can now know what pixel is actually relevant, however the *objectness* and *backgroundness* can provide a rough measure of this in an aggregated way
  
  Let’s assume at some point in your processing pipeline, you have 2 regions containing the same car but at different scales: so ideally both of them should be associated with good confidence to CarID but it’s clear one will contain much more background pixels than the other one and you'd like this to be represented as an additional measure
  
  > **Comments:**
  > **username_0**: That's a fantastic answer for a year-old question. Thanks!  
  
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