  This paper tackles the problem of semi-supervised video object segmentation
on resource-constrained devices, such as mobile phones. We formulate this
problem as a distillation task, whereby we demonstrate that small
space-time-memory networks with finite memory can achieve competitive results
with state of the art, but at a fraction of the computational cost (32
milliseconds per frame on a Samsung Galaxy S22). Specifically, we provide a
theoretically grounded framework that unifies knowledge distillation with
supervised contrastive representation learning. These models are able to
jointly benefit from both pixel-wise contrastive learning and distillation from
a pre-trained teacher. We validate this loss by achieving competitive J&F to
state of the art on both the standard DAVIS and YouTube benchmarks, despite
running up to 5x faster, and with 32x fewer parameters.
