A question on your implementation for FGFA paper about mega.pytorch

[Scalsol]

Hi, thank you for your interest! this implementation is the same as it is in the original FGFA repo. And in the paper it also says "By default, we sample 2 frames in training and aggregate over 21 frames in inference.", so in training, the current frame is not accumulated. But it could be sampled by random sampling.

[jcliu0428]

Hi, Thank you for your answer! I also notice this in the original paper. By the way, I have another question. I notice both your reimplementation and official MXNet code multiply the flownet output by 2.5. But in original flownet code, I have not seen this line. Could you tell me why the output flow need to multiply by 2.5?

[Scalsol]

I just follow the implementation of the original repo so I also don't know why :) Maybe you should ask the author of FGFA paper or you could try to remove the 2.5 factor and see whether the performance will drop.