[redox99]

Yes it would, or at least a similar operation.

The point of using a CNN instead of a FCN is that you force it to learn in a certain way that prevents overfitting. But given a sufficient dataset, and proper data augmentation you would expect a FCN to be able to identify objects regardless of translation. It's just that a CNN would train easier and better, with a smaller network (a FCN doing convolutions would be very wasteful).

That's why traditionally you would pick your architecture to help it learn in a certain way (images=cnn, text=rnn/lstm/gru). But the nice thing about transformers is that they are more general.