Thinking about deformable convolutions, some things I found different interpretations of:
-) Does the offset change for each individual k x k kernel or is it fixed for the whole image? Would this mean that pixels could potentially overlap?
-) Is the same offset then applied for each input layer, ie. AxBxC where C might be any number of filters.
-) During inference, keeping the offset generating layers in the network, each k x k kernel would experience an individual offset, or would the offset be the same for the whole image?
Thinking about deformable convolutions, some things I found different interpretations of:
-) Does the offset change for each individual k x k kernel or is it fixed for the whole image? Would this mean that pixels could potentially overlap?
-) Is the same offset then applied for each input layer, ie. AxBxC where C might be any number of filters.
-) During inference, keeping the offset generating layers in the network, each k x k kernel would experience an individual offset, or would the offset be the same for the whole image?