mlsanityData to 7 Oct 2026

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

arXiv 2306.02291 ↗First posted 4 Jun 2023cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 4 Jun 2023.

Builds on

19 of 23 references.

References · 19

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7 more
  1. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 479
  2. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Segment Anything for Videos: A Systematic Surveycited by 5