SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in
  Videos by Prompt Denoising
Tao Zhou Wenhan Luo Qi Ye Zhiguo Shi Jiming Chen
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.