  Event cameras have recently gained significant traction since they open up
new avenues for low-latency and low-power solutions to complex computer vision
problems. To unlock these solutions, it is necessary to develop algorithms that
can leverage the unique nature of event data. However, the current
state-of-the-art is still highly influenced by the frame-based literature, and
usually fails to deliver on these promises. In this work, we take this into
consideration and propose a novel self-supervised learning pipeline for the
sequential estimation of event-based optical flow that allows for the scaling
of the models to high inference frequencies. At its core, we have a
continuously-running stateful neural model that is trained using a novel
formulation of contrast maximization that makes it robust to nonlinearities and
varying statistics in the input events. Results across multiple datasets
confirm the effectiveness of our method, which establishes a new state of the
art in terms of accuracy for approaches trained or optimized without ground
truth.
