Conventional video object segmentation (VOS) methods usually necessitate a
substantial volume of pixel-level annotated video data for fully supervised
learning. In this paper, we present HVC, a \textbf{h}ybrid static-dynamic
\textbf{v}isual \textbf{c}orrespondence framework for self-supervised VOS. HVC
extracts pseudo-dynamic signals from static images, enabling an efficient and
scalable VOS model. Our approach utilizes a minimalist fully-convolutional
architecture to capture static-dynamic visual correspondence in image-cropped
views. To achieve this objective, we present a unified self-supervised approach
to learn visual representations of static-dynamic feature similarity. Firstly,
we establish static correspondence by utilizing a priori coordinate information
between cropped views to guide the formation of consistent static feature
representations. Subsequently, we devise a concise convolutional layer to
capture the forward / backward pseudo-dynamic signals between two views,
serving as cues for dynamic representations. Finally, we propose a hybrid
visual correspondence loss to learn joint static and dynamic consistency
representations. Our approach, without bells and whistles, necessitates only
one training session using static image data, significantly reducing memory
consumption ($\sim$16GB) and training time ($\sim$\textbf{2h}). Moreover, HVC
achieves state-of-the-art performance in several self-supervised VOS benchmarks
and additional video label propagation tasks.

本文介绍了一种名为 HVC 的混合静态动态视觉对应框架，用于自监督视频对象分割。HVC 从静态图像中提取伪动态信号，实现了高效且可扩展的 VOS 模型。该方法利用极简的全卷积架构在图像裁剪视图中捕捉静态动态视觉对应关系，并通过统一的自监督方法学习静态 - 动态特征相似性的视觉表示，从而实现联合静态和动态一致性表示的学习。HVC 仅需要一次训练会话使用静态图像数据，显著减少内存消耗（约 16GB）和训练时间（约 2 小时），并在多个自监督 VOS 基准和附加视频标签传播任务中实现了最新的性能。