Most image recognition neural networks use Relu activations which is just a linear output unless the input is below zero. Even sigmoids/tanh are most linear in the middle region, and weight penalties are used to keep the weights small so they stay in that region.

But it doesn't really matter what activation function you use. The paper argues that its the linear layers between the nonlinearities that are the problem.