How to get Euclidean Distance between feature maps
The Euclidean between two images $p$ and $q$ can be calculated as follows:
$d(p, q) = \sqrt{(q_1 - p_1)^2 + (q_2 - p_2)^2 + ... + (q_{49} - p_{49})^2}$
which is the distance between the 49 (7x7) features of the two images.
This should then give you a vector of shape (1024, 1) where each value is the Euclidean distance of the feature maps of the previous image, with the first one being all NA since it's the first image. Then a convolution is applied with a window of 4 with the vector/kernel [0.1, 0.1, 0.1, 0.99], which basically multiplies 4 values of the (1024, 1) vector by the kernel values and adds them up. I.e. given the first 4 values of the (1024, 1) vector of [0.3, 0.5, 0.2, 0.4] the result of this multiplication would be:
$0.3 * 0.1 + 0.5 * 0.1 + 0.2 * 0.1 + 0.99 * 0.4 = 0.03 + 0.05 + 0.02 + 3.96 = 4.06$