I have read the paper and was wondering if there is a fix for the problem stated on page 8:

Analysis of misclassified examples revealed that DSNT was less accurate for predicting edge case joints that lie very close to the image boundary, which is expected due to how the layer works

The reason seems to be that the X and Y grid is defined to lie in the range (-1,1) by the formulas on page 4. Is there a specific reason for this or would the DSNT also work when the grids are in the range [-1,1]?

A formula to define such a grid would be

-1 + (2*(i-1)) / (w-1)

For a heatmap that has the width 5, the grid would have these values in the columns:

i=1 => -1
i=2 => -1 + 2/4 = -0.5
i=3 => -1 + 4/4 = 0
i=4 => -1 + 6/4 = 0.5
i=5 => -1 + 8/4 = 1

So the grid would look like
-1 | -0.5 | 0 | 0.5 | 1

instead of
-0.8 | -0.4 | 0 | 0.4 | 0.8

So my question is if there is a reason to use the second grid instead of the first one? From what I see this should also work. If there is interest in this change, I could try to implement it.

The advantage would be that the system will be able to regress coordinates on the border and not just very close to the border (depending on the heatmap dimensions)