Fair Latency-Aware Metric for real-time video segmentation networks

As supervised semantic segmentation is reaching satisfying results, many recent papers focused on making segmentation network architectures faster, smaller and more efficient. In particular, studies often aim to reach the stage to which they can claim to be "real-time". Achieving this goal is especially relevant in the context of real-time video operations for autonomous vehicles and robots, or medical imaging during surgery. The common metric used for assessing these methods is so far the same as the ones used for image segmentation without time constraint: mean Intersection over Union (mIoU). In this paper, we argue that this metric is not relevant enough for real-time video as it does not take into account the processing time (latency) of the network. We propose a similar but more relevant metric called FLAME for video-segmentation networks, that compares the output segmentation of the network with the ground truth segmentation of the current video frame at the time when the network finishes the processing. We perform experiments to compare a few networks using this metric and propose a simple addition to network training to enhance results according to that metric.