Thank you for the awesome work again.
This work is very inspiring.
I have a question about the ablation study on the spatially-variant operation (Figure 9 (c) in the paper).
Does this mean that f(x_p, p, phi_p; phi) is less effective than f(x_p, p; phi_p)? (where phi is spatially-invariant learnable parameter).
If so, why?
Note1: In the case of f(x_p, p, phi_p; phi), the dimension for the phi_p should be much smaller since it now works as an input to the network.
Note2: If we use f(x_p, p, phi_p; phi), I think it would be possible to find an analogy with LIIF model (which tackles arbitrary-scale SR problem). In other words, reversely, I think it is also possible to apply this paper's pixelwise MLP method to arbitrary-scale SR problem if directly predicting the MLP parameters is more efficient than putting the the feature as an input for the coordinate-based MLP.
Thank you for the awesome work again.
This work is very inspiring.
I have a question about the ablation study on the spatially-variant operation (Figure 9 (c) in the paper).
Does this mean that f(x_p, p, phi_p; phi) is less effective than f(x_p, p; phi_p)? (where phi is spatially-invariant learnable parameter).
If so, why?
Note1: In the case of f(x_p, p, phi_p; phi), the dimension for the phi_p should be much smaller since it now works as an input to the network.
Note2: If we use f(x_p, p, phi_p; phi), I think it would be possible to find an analogy with LIIF model (which tackles arbitrary-scale SR problem). In other words, reversely, I think it is also possible to apply this paper's pixelwise MLP method to arbitrary-scale SR problem if directly predicting the MLP parameters is more efficient than putting the the feature as an input for the coordinate-based MLP.