  Semantic segmentation is a well-addressed topic in the computer vision
literature, but the design of fast and accurate video processing networks
remains challenging. In addition, to run on embedded hardware, computer vision
models often have to make compromises on accuracy to run at the required speed,
so that a latency/accuracy trade-off is usually at the heart of these real-time
systems' design. For the specific case of videos, models have the additional
possibility to make use of computations made for previous frames to mitigate
the accuracy loss while being real-time.
  In this work, we propose to tackle the task of fast future video segmentation
prediction through the use of convolutional layers with time-dependent channel
masking. This technique only updates a chosen subset of the feature maps at
each time-step, bringing simultaneously less computation and latency, and
allowing the network to leverage previously computed features. We apply this
technique to several fast architectures and experimentally confirm its benefits
for the future prediction subtask.
