Online unsupervised video object segmentation (UVOS) uses the previous frames
as its input to automatically separate the primary object(s) from a streaming
video without using any further manual annotation. A major challenge is that
the model has no access to the future and must rely solely on the history,
i.e., the segmentation mask is predicted from the current frame as soon as it
is captured. In this work, a novel contrastive motion clustering algorithm with
an optical flow as its input is proposed for the online UVOS by exploiting the
common fate principle that visual elements tend to be perceived as a group if
they possess the same motion pattern. We build a simple and effective
auto-encoder to iteratively summarize non-learnable prototypical bases for the
motion pattern, while the bases in turn help learn the representation of the
embedding network. Further, a contrastive learning strategy based on a boundary
prior is developed to improve foreground and background feature discrimination
in the representation learning stage. The proposed algorithm can be optimized
on arbitrarily-scale data i.e., frame, clip, dataset) and performed in an
online fashion. Experiments on $\textit{DAVIS}_{\textit{16}}$, $\textit{FBMS}$,
and $\textit{SegTrackV2}$ datasets show that the accuracy of our method
surpasses the previous state-of-the-art (SoTA) online UVOS method by a margin
of 0.8%, 2.9%, and 1.1%, respectively. Furthermore, by using an online deep
subspace clustering to tackle the motion grouping, our method is able to
achieve higher accuracy at $3\times$ faster inference time compared to SoTA
online UVOS method, and making a good trade-off between effectiveness and
efficiency.

本文提出了一种基于对比学习的无监督在线视频目标分割算法，利用光流作为输入，通过聚类的方式学习物体运动信息，并采用边界先验进行对比学习以提高特征鉴别度和分割精度，该算法在多个数据集上取得了超过现有方法的精度并在效率和准确性之间达到了良好的平衡。