Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of available datasets. The Segment Anything Model (SAM) has introduced a new prompt-driven paradigm for image segmentation, unlocking a range of previously unexplored capabilities. In this paper, we propose a novel paradigm called UVOSAM, which leverages SAM for unsupervised video object segmentation without requiring video mask labels. To address SAM's limitations in instance discovery and identity association, we introduce a video salient object tracking network that automatically generates trajectories for prominent foreground objects. These trajectories then serve as prompts for SAM to produce video masks on a frame-by-frame basis. Our experimental results demonstrate that UVOSAM significantly outperforms current mask-supervised methods. These findings suggest that UVOSAM has the potential to improve unsupervised video object segmentation and reduce the cost of manual annotation.

UVOSAM是一种新的无监督视频对象分割范例，利用Segment Anything Model和视频显著对象跟踪网络来生成显着前景对象的轨迹作为提示来对每一帧视频进行对象分割。实验结果显示，UVOSAM显著优于当前基于标记方法的分割方法。

UVOSAM: 无需蒙版的无监督视频对象分割范式，通过任意分割模型实现