Name of paper for encoding/representing XY coordinates in deep learning 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, 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?
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