CNNs: Why do the filters identify more complex features as the model goes on?
In a convolution neural network, every neuron in layer $i+1$ depends on few neurons in layer $i$. It thus aggregated local information for layer $i$. As you go through the layers, the subset of input that the neuron sees grows, and so the features depend on a larger part of the input. Eventually, neurons depend, in directly, on all inputs.