  Video data is often repetitive; for example, the contents of adjacent frames
are usually strongly correlated. Such redundancy occurs at multiple levels of
complexity, from low-level pixel values to textures and high-level semantics.
We propose Event Neural Networks (EvNets), which leverage this redundancy to
achieve considerable computation savings during video inference. A defining
characteristic of EvNets is that each neuron has state variables that provide
it with long-term memory, which allows low-cost, high-accuracy inference even
in the presence of significant camera motion. We show that it is possible to
transform a wide range of neural networks into EvNets without re-training. We
demonstrate our method on state-of-the-art architectures for both high- and
low-level visual processing, including pose recognition, object detection,
optical flow, and image enhancement. We observe roughly an order-of-magnitude
reduction in computational costs compared to conventional networks, with
minimal reductions in model accuracy.
