I want to find 1D correlation between two matrices. These two matrices are the output of a convolution operation on two different images. Let's call the first matrix as matrix A and the other one as matrix B. Both these matrices have the shape 100 x 100 x 64 (say).
I've been following a research paper which basically computes 1D correlation between these two matrices (matrix A and matrix B) in one of the steps and the output of the correlation operation is also a matrix with the shape 100 x 100 x 64. The link to the paper can be found here. The network can be found on Page 4. The correlation part is in the bottom part of the network. A couple of lines have been mentioned about it in the 2nd paragraph of section 3.3 (on the same page, below the network).
I am not really sure what they mean by 1D correlation and more so how to implement it in Python. I am also confused as to how the shape of the output remains the same as the input after applying correlation. I am using the PyTorch library for implementing this network.
Any help will be appreciated. Thanks.
答案 0 :(得分：1)
So they basically have 1 original image, which they treat as the left side view for the depth perception algorithm, but since you need stereo vision to calculate depth in a still image they use a neural structure to synthesise a right side view.
1 Dimensional Correlation takes 2 sequences and calculates the correlation at each point giving you another 1D sequence of the same length as the 2 inputs. So if you apply this correlation along a certain axis of a tensor the resultant tensor does not change shape.
Intuitively they thought it made sense to correlate the images along the horizontal axes a bit like reading the images like reading a book, but in this instance it should have an effect akin to identifying that things that are further away also appear to be points that are closer together in the left and right side views. The correlation is probably higher for left and right side data-points that are further away and this makes the depth classification for the neural network much easier.