[R] [1711.07971] Non-local Neural Networks • r/MachineLearning

So if I'm understanding this paper correctly, the primary way that these non-local blocks differ from a fully connected net, is that when calculating y_i, you are also able to take into account the input at x_i (not just x_j). Thus, you're able to capture relationships in your image between non-local x_i and x_j, instead of merely local relationships through conv filters. For example, if you ignore x_i in your function and set f(x_i, x_j) W_ij * x_j, then this non-local block essentially approximates a fully connected layer. One thing that confuses me is that it seems like you're throwing away your relative positional data. Is the idea that this relative positional data is already captured through conv nets?