  Motion Expression guided Video Segmentation is a challenging task that aims
at segmenting objects in the video based on natural language expressions with
motion descriptions. Unlike the previous referring video object segmentation
(RVOS), this task focuses more on the motion in video content for
language-guided video object segmentation, requiring an enhanced ability to
model longer temporal, motion-oriented vision-language data. In this report,
based on the RVOS methods, we successfully introduce mask information obtained
from the video instance segmentation model as preliminary information for
temporal enhancement and employ SAM for spatial refinement. Finally, our method
achieved a score of 49.92 J &F in the validation phase and 54.20 J &F in the
test phase, securing the final ranking of 2nd in the MeViS Track at the CVPR
2024 PVUW Challenge.
