Thank you for the insights, learned a lot from you. Yes, the idea is to make the local processing path to compensate the missing information from the global processing path (where the image is processed in a holistic manner), while the trained submodels in the local processing path  to be trained with sofmax-ed trained combination weights.
So basically, as you said, I can use only one loss function across all parts (bigger capacity model) or either several losses where each of them corresponds to a single patch?
What’s more meaningful to you, which way to go, many outputs from the local branch, where each output is multiplied by a learnable weight while optimizing with several losses (each patch one loss), or weighted average ensemble trained with a single loss function?