  The objective of this paper is self-supervised learning of video object
segmentation. We develop a unified framework which simultaneously models
cross-frame dense correspondence for locally discriminative feature learning
and embeds object-level context for target-mask decoding. As a result, it is
able to directly learn to perform mask-guided sequential segmentation from
unlabeled videos, in contrast to previous efforts usually relying on an oblique
solution - cheaply "copying" labels according to pixel-wise correlations.
Concretely, our algorithm alternates between i) clustering video pixels for
creating pseudo segmentation labels ex nihilo; and ii) utilizing the pseudo
labels to learn mask encoding and decoding for VOS. Unsupervised correspondence
learning is further incorporated into this self-taught, mask embedding scheme,
so as to ensure the generic nature of the learnt representation and avoid
cluster degeneracy. Our algorithm sets state-of-the-arts on two standard
benchmarks (i.e., DAVIS17 and YouTube-VOS), narrowing the gap between self- and
fully-supervised VOS, in terms of both performance and network architecture
design.
