<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>

CC BY-SA 4.0
2019-05-13T19:55:12.823Z
Looks like this is not "the" paper, but it's a good find, thanks
Benjamin Crouzier
CC BY-SA 4.0
2019-05-13T17:27:44.853Z
2019-05-13T17:27:44.853Z
mshlis
<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>

CC BY-SA 4.0
2019-05-01T16:47:20.437Z
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.
Hanzy
CC BY-SA 4.0
2019-05-01T17:18:04.533Z
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
Jaden Travnik
<neural-networks><deep-learning><computer-vision><papers>
Name of paper for encoding/representing XY coordinates in deep learning
Benjamin Crouzier
# 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  

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## 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  

---

