deformable-convolution-pytorch
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Different interpretation of def_conv:
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?
did you find any answers?