Answers.Body: <p>So this paper is by google, but is very similar where they use 2D positional embeddings and perform MHA on the flattened image. Are you talking about <a href="https://arxiv.org/pdf/1904.09925.pdf" rel="nofollow noreferrer">Attention Augmented Convolutional Networks</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-13T19:55:12.823Z
Answers.Text: Looks like this is not "the" paper, but it's a good find, thanks
Answers.UserDisplayName: Benjamin Crouzier
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Answers.CreationDate: 2019-05-13T17:27:44.853Z
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Answers.UserDisplayName: mshlis
Body: <p>It this podcast between Oriol Vinyals and Lex Friedman: <a href="https://youtu.be/Kedt2or9xlo?t=1769" rel="noreferrer">https://youtu.be/Kedt2or9xlo?t=1769</a>, at 29:29, Oriol Vinyals refers to a paper:</p>
  
  <blockquote>
    <p>If you look at research in computer vision where it makes a lot of sense to treat images as two dimensional arrays...
    There is actually a very nice paper from Facebook. I forgot who the
    authors are but I think [it's] part of Kaiming He's group. And what
    they do is they take an image, which is a 2D signal, and they
    actually take pixel by pixel, and scramble the image, as if it
    was a just a list of pixels, crucially they encode the position of the pixels
    with the XY coordinates. And this is a new architecture which we
    incidentally also use in Starcraft 2 called the transformer, which is
    a very popular paper from last year which yielded very nice results in
    machine translation.</p>
  </blockquote>
  
  <p>Do you know which paper he is referring to? </p>
  
  <p>I'm guessing maybe he is talking about <a href="https://arxiv.org/abs/1711.07971" rel="noreferrer">non-local neural networks</a>, but I'm probably guessing wrong.</p>
  
  <p><em>Edit: after reviewing the recent publications of Kaiming He (<a href="http://kaiminghe.com/" rel="noreferrer">http://kaiminghe.com/</a>), maybe I'm guessing right. Any thoughts?</em></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-01T16:47:20.437Z
Comments.Text: If you verify please do update. I’d like to read that, too. Also, does this podcast have show notes? Sometimes they include links to referenced papers / blogs, etc.
Comments.UserDisplayName: Hanzy
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-01T17:18:04.533Z
Comments.Text: This isnt the paper but it sounds related. "[An intriguing failing of convolutional neural networks and the CoordConv solution][1]" from Uber AI Labs.
  
  They also have a [really informative video][2] and a [repo][3].
  
  
    [1]: https://arxiv.org/pdf/1807.03247.pdf
    [2]: https://www.youtube.com/watch?v=8yFQc6elePA
    [3]: https://github.com/uber-research/CoordConv
Comments.UserDisplayName: Jaden Travnik
Tags: <neural-networks><deep-learning><computer-vision><papers>
Title: Name of paper for encoding/representing XY coordinates in deep learning
UserDisplayName: Benjamin Crouzier
ThreadText: # Name of paper for encoding/representing XY coordinates in deep learning
  
  (Asked by: username_0 on 2019-05-01)
  
  It this podcast between Oriol Vinyals and Lex Friedman: <https://youtu.be/Kedt2or9xlo?t=1769>, at 29:29, Oriol Vinyals refers to a paper:
  
  > If you look at research in computer vision where it makes a lot of sense to treat images as two dimensional arrays...
  > There is actually a very nice paper from Facebook. I forgot who the
  > authors are but I think [it's] part of Kaiming He's group. And what
  > they do is they take an image, which is a 2D signal, and they
  > actually take pixel by pixel, and scramble the image, as if it
  > was a just a list of pixels, crucially they encode the position of the pixels
  > with the XY coordinates. And this is a new architecture which we
  > incidentally also use in Starcraft 2 called the transformer, which is
  > a very popular paper from last year which yielded very nice results in
  > machine translation.
  
  Do you know which paper he is referring to?
  
  I'm guessing maybe he is talking about [non-local neural networks](https://arxiv.org/abs/1711.07971), but I'm probably guessing wrong.
  
  *Edit: after reviewing the recent publications of Kaiming He (<http://kaiminghe.com/>), maybe I'm guessing right. Any thoughts?*
  
  **Tags:** neural-networks, deep-learning, computer-vision, papers
  > **Comments:**
  > **username_1**: If you verify please do update. I’d like to read that, too. Also, does this podcast have show notes? Sometimes they include links to referenced papers / blogs, etc.  
  > **username_2**: This isnt the paper but it sounds related. "[An intriguing failing of convolutional neural networks and the CoordConv solution][1]" from Uber AI Labs.  They also have a [really informative video][2] and a [repo][3].     [1]: https://arxiv.org/pdf/1807.03247.pdf   [2]: https://www.youtube.com/watch?v=8yFQc6elePA   [3]: https://github.com/uber-research/CoordConv  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2019-05-13. Score: 2
  
  So this paper is by google, but is very similar where they use 2D positional embeddings and perform MHA on the flattened image. Are you talking about [Attention Augmented Convolutional Networks](https://arxiv.org/pdf/1904.09925.pdf)
  
  > **Comments:**
  > **username_0**: Looks like this is not "the" paper, but it's a good find, thanks  
  
  ---
  
  