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

• URL: http://arxiv.org/abs/2306.02291v2
• Date: Tue, 6 Jun 2023 01:49:09 GMT
• 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
• Abstract summary: In this paper, we introduce 3rd place solution for PVUW2023 VSS track. We have explored various image-level visual backbones and segmentation heads to tackle the problem of video semantic segmentation.
• Score: 68.56017675820897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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