title: Can someone explain the correlation layer in FlowNet in a simple way?
body: <p>I am currently reading the paper "FlowNet: Learning Optical Flow with Convolutional Networks" and having trouble understanding the correlation layer. </p>
  
  <p>I can't seem to find any explanation on google, so I thought I should ask her:</p>
  
  <ol>
  <li><p>When the paper talks about comparing each patch from f_1 to each patch from f_2, where f_1 and f_2 are feature maps of dimension w<em>h</em>c, what do they mean by patch? Are we talking about a patch of features from a feature map or a patch of pixels from one of the original images?</p></li>
  <li><p>what is x_1 and x_2? Are they a feature pixel (1*1*c) in the feature maps? are they coordinate values?</p></li>
  <li><p>What does f_1(x_1 + o) mean exactly?</p></li>
  </ol>
  
  <p>Many thanks!</p>