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。

自监督视频分割的统一掩模嵌入和对应关系学习

Unified Mask Embedding and Correspondence Learning for Self-Supervised  Video Segmentation

We introduce the first completely unsupervised correspondence learning
approach for deformable 3D shapes. Key to our model is the understanding that
natural deformations (such as changes in pose) approximately preserve the
metric structure of the surface, yielding a natural criterion to drive the
learning process toward distortion-minimizing predictions. On this basis, we
overcome the need for annotated data and replace it by a purely geometric
criterion. The resulting learning model is class-agnostic, and is able to
leverage any type of deformable geometric data for the training phase. In
contrast to existing supervised approaches which specialize on the class seen
at training time, we demonstrate stronger generalization as well as
applicability to a variety of challenging settings. We showcase our method on a
wide selection of correspondence benchmarks, where we outperform other methods
in terms of accuracy, generalization, and efficiency.

介绍了第一种完全无监督的对变形三维图形进行对应学习的方法，通过理解自然变形的形态变化近似保持曲面的度量结构，提出了一种驱动学习过程的自然准则，从而克服了对带注释数据的需求，并取而代之的是一种纯几何标准，学习模型不分含义类别，能够利用任何类型的可变形几何数据进行训练，相对于现有的专门针对训练期间观察到的类别的监督方法，我们展示出更强的泛化能力和应用于各种具有挑战性设置的能力，在广泛的对应基准测试中展示了我们的方法，在精度、泛化能力和效率方面均超过了其他方法。