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.

通过开发一个统一的框架，其中同时建模跨帧密集对应以进行本地区分特征学习并嵌入对象级上下文进行目标掩码解码，从而使得能够直接从未标记的视频中学习执行基于掩码的连续分割，而不是依赖于基于像素的关联的廉价 “复制” 标签的非直接方法。