  One of the greatest challenges in the design of a real-time perception system
for autonomous driving vehicles and drones is the conflicting requirement of
safety (high prediction accuracy) and efficiency. Traditional approaches use a
single frame rate for the entire system. Motivated by the observation that the
lack of robustness against environmental factors is the major weakness of
compact ConvNet architectures, we propose a dual frame-rate system that brings
in the best of both worlds: A modulator stream that executes an expensive
models robust to environmental factors at a low frame rate to extract slowly
changing features describing the environment, and a prediction stream that
executes a light-weight model at real-time to extract transient signals that
describes particularities of the current frame. The advantage of our design is
validated by our extensive empirical study, showing that our solution leads to
consistent improvements using a variety of backbone architecture choice and
input resolutions. These findings suggest multiple frame-rate systems as a
promising direction in designing efficient perception for autonomous agents.
