Recently, promptable segmentation models, such as the Segment Anything Model
(SAM), have demonstrated robust zero-shot generalization capabilities on static
images. These promptable models exhibit denoising abilities for imprecise
prompt inputs, such as imprecise bounding boxes. In this paper, we explore the
potential of applying SAM to track and segment objects in videos where we
recognize the tracking task as a prompt denoising task. Specifically, we
iteratively propagate the bounding box of each object's mask in the preceding
frame as the prompt for the next frame. Furthermore, to enhance SAM's denoising
capability against position and size variations, we propose a multi-prompt
strategy where we provide multiple jittered and scaled box prompts for each
object and preserve the mask prediction with the highest semantic similarity to
the template mask. We also introduce a point-based refinement stage to handle
occlusions and reduce cumulative errors. Without involving tracking modules,
our approach demonstrates comparable performance in video object/instance
segmentation tasks on three datasets: DAVIS2017, YouTubeVOS2018, and UVO,
serving as a concise baseline and endowing SAM-based downstream applications
with tracking capabilities.

本文探讨了将 Segment Anything Model（SAM）应用于视频对象跟踪和分割任务的潜力，通过使用多种 prompt 和引入基于点的优化阶段，实现了在三个数据集上对比性能相当的视频对象 / 实例分割任务，该方法为基于 SAM 的后续应用赋予了跟踪能力。