You are welcome.
I think so, actually if you read the paper that introduces kaiming or he, its main advantage is that defines uniform distribution in the way that enables deep NNs with many layers to converge faster, so it is kind of super class of basic uniform as it can be obtained from kaiming_uniform. So, that is why I think if you define a small model using this approach, it won’t hurt the model’s performance and maybe because of this logic, it is the default init method(simplicity purposes without loosing performance).
But as you mentioned, logically gain for different activation functions are different and need to be incorporated.