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.
@article{arxiv.2303.07815,
title = {MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation},
author = {Roy Miles and Mehmet Kerim Yucel and Bruno Manganelli and Albert Saa-Garriga},
journal= {arXiv preprint arXiv:2303.07815},
year = {2023}
}