As i far as i understand, your network reimplements GMM from this paper: https://arxiv.org/pdf/1703.05593.pdf . In the original GMM's paper the architecture was:
Stage 1:
ImageA -> featureExtraction -> fA
ImageB -> featureExtraction -> fB
then matching(fA, fB) -> AffineRegresstion(CAB) -> ThetaAff , then warp to imageA to become ImageWarpA.
Stage 2:
Repeat over featureExtraction ImageWarpA , then matching(fImageWarpA, fB) -> TBS Regression -> ThetaTPS.
Which matching is correlation layers plus L2-Norm.
However, in your network you implemented like this (please see the attached)
Can you please explain why it that, or just point out where i am missing you in the paper?
Dear Mr @sergeywong ,
As i far as i understand, your network reimplements GMM from this paper: https://arxiv.org/pdf/1703.05593.pdf . In the original GMM's paper the architecture was: Stage 1: ImageA -> featureExtraction -> fA ImageB -> featureExtraction -> fB
then matching(fA, fB) -> AffineRegresstion(CAB) -> ThetaAff , then warp to imageA to become ImageWarpA.
Stage 2: Repeat over featureExtraction ImageWarpA , then matching(fImageWarpA, fB) -> TBS Regression -> ThetaTPS. Which matching is correlation layers plus L2-Norm.
However, in your network you implemented like this (please see the attached)![Screen Shot 2019-11-27 at 10 13 40](https://user-images.githubusercontent.com/15938335/69690521-bb664200-10fe-11ea-8b8b-8ec7d81aa143.png)
Can you please explain why it that, or just point out where i am missing you in the paper?
Thank you for your time. Cheers!