Question about PFG layer

[BlueDing101]

Brilliant work and thanks for the open source code.

I'm now reading the paper and code about the DBG model and I have a question about the PFG layer.

According to the paper and the code, the parameter w_l and w_r for the the PFG layer's output at location (t_s, t_e, n, c), which corresponding to the w, h, t, c in the code if I have understood correctly, can be directly calculated under the formula (1)(2)(3)(4) in the "Proposal feature generation layer" section. But both the paper and code shows that this layer is a trainable layer. So will the w_l and w_r be updated during the backward? Since it looks like a variable that don't need to be trained rather than a trainable parameter to me and I'm confused about this. Could you please explain this? Thank you very much!

[linchuming]

Thanks for your attention!
There are not trainable parameters in the PFG layer. You can treat the PFG layer is a bilinear sampling operation. The parameter w_l and w_r are computed as bilinear interpolation.

[BlueDing101]

Thanks for your attention!
There are not trainable parameters in the PFG layer. You can treat the PFG layer is a bilinear sampling operation. The parameter w_l and w_r are computed as bilinear interpolation.

Thanks for your reply. I can get it! Thank you so much!