  In this paper, we introduce 3rd place solution for PVUW2023 VSS track.
Semantic segmentation is a fundamental task in computer vision with numerous
real-world applications. We have explored various image-level visual backbones
and segmentation heads to tackle the problem of video semantic segmentation.
Through our experimentation, we find that InternImage-H as the backbone and
Mask2former as the segmentation head achieves the best performance. In
addition, we explore two post-precessing methods: CascadePSP and Segment
Anything Model (SAM). Ultimately, our approach obtains 62.60\% and 64.84\% mIoU
on the VSPW test set1 and final test set, respectively, securing the third
position in the PVUW2023 VSS track.
