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Link to original content: https://doi.org/10.48550/arXiv.2306.02291
[2306.02291] 3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2306.02291 (cs)
[Submitted on 4 Jun 2023 (v1), last revised 6 Jun 2023 (this version, v2)]

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

Authors:Shijie Chang, Zeqi Hao, Ben Kang, Xiaoqi Zhao, Jiawen Zhu, Zhenyu Chen, Lihe Zhang, Lu Zhang, Huchuan Lu
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Abstract: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.
Comments: 3rd Place Solution for CVPR 2023 PVUW VSS Track
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2306.02291 [cs.CV]
  (or arXiv:2306.02291v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2306.02291

Submission history

From: Shijie Chang [view email]
[v1] Sun, 4 Jun 2023 07:50:38 UTC (194 KB)
[v2] Tue, 6 Jun 2023 01:49:09 UTC (194 KB)
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