  The goal of this paper is to discover, segment, and track independently
moving objects in complex visual scenes. Previous approaches have explored the
use of optical flow for motion segmentation, leading to imperfect predictions
due to partial motion, background distraction, and object articulations and
interactions. To address this issue, we introduce an appearance-based
refinement method that leverages temporal consistency in video streams to
correct inaccurate flow-based proposals. Our approach involves a simple
selection mechanism that identifies accurate flow-predicted masks as exemplars,
and an object-centric architecture that refines problematic masks based on
exemplar information. The model is pre-trained on synthetic data and then
adapted to real-world videos in a self-supervised manner, eliminating the need
for human annotations. Its performance is evaluated on multiple video
segmentation benchmarks, including DAVIS, YouTubeVOS, SegTrackv2, and FBMS-59.
We achieve competitive performance on single-object segmentation, while
significantly outperforming existing models on the more challenging problem of
multi-object segmentation. Finally, we investigate the benefits of using our
model as a prompt for a per-frame Segment Anything Model.
