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In this paper, we introduce 3rd place solution for PVUW2023 VSS track.Semantic segmentation is a fundamental task in computer vision with numerousreal-world applications. We have explored various image-level visual backbonesand segmentation heads to tackle the problem of video semantic segmentation.Through our experimentation, we find that InternImage-H as the backbone andMask2former as the segmentation head achieves the best performance. Inaddition, we explore two post-precessing methods: CascadePSP and SegmentAnything Model (SAM). Ultimately, our approach obtains 62.60\% and 64.84\% mIoUon the VSPW test set1 and final test set, respectively, securing the thirdposition in the PVUW2023 VSS track.

3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW