  The current state-of-the-art methods for unsupervised video object
segmentation (UVOS) require extensive training on video datasets with mask
annotations, limiting their effectiveness in handling challenging scenarios.
However, the Segment Anything Model (SAM) introduces a new prompt-driven
paradigm for image segmentation, offering new possibilities. In this study, we
investigate SAM's potential for UVOS through different prompt strategies. We
then propose UVOSAM, a mask-free paradigm for UVOS that utilizes the STD-Net
tracker. STD-Net incorporates a spatial-temporal decoupled deformable attention
mechanism to establish an effective correlation between intra- and inter-frame
features, remarkably enhancing the quality of box prompts in complex video
scenes. Extensive experiments on the DAVIS2017-unsupervised and
YoutubeVIS19\&21 datasets demonstrate the superior performance of UVOSAM
without mask supervision compared to existing mask-supervised methods, as well
as its ability to generalize to weakly-annotated video datasets. Code can be
found at https://github.com/alibaba/UVOSAM.
