Video Panoptic Segmentation (VPS) is a challenging task that is extends from
image panoptic segmentation.VPS aims to simultaneously classify, track, segment
all objects in a video, including both things and stuff. Due to its wide
application in many downstream tasks such as video understanding, video
editing, and autonomous driving. In order to deal with the task of video
panoptic segmentation in the wild, we propose a robust integrated video
panoptic segmentation solution. We use DVIS++ framework as our baseline to
generate the initial masks. Then,we add an additional image semantic
segmentation model to further improve the performance of semantic
classes.Finally, our method achieves state-of-the-art performance with a VPQ
score of 56.36 and 57.12 in the development and test phases, respectively, and
ultimately ranked 2nd in the VPS track of the PVUW Challenge at CVPR2024.

我们提出了一个强大的整合视频全景分割解决方案，该方案基于 DVIS ++ 框架生成初始掩模，然后添加了额外的图像语义分割模型以进一步提高语义类别的性能。最终，我们的方法在视频全景分割任务中取得了 56.36 和 57.12 的 VPQ 得分，分别在开发和测试阶段排名第二。

2024 年 PVUW 挑战赛第二名解决方案：视频全景分割

2nd Place Solution for PVUW Challenge 2024: Video Panoptic Segmentation

Video panoptic segmentation is a challenging task that serves as the
cornerstone of numerous downstream applications, including video editing and
autonomous driving. We believe that the decoupling strategy proposed by DVIS
enables more effective utilization of temporal information for both "thing" and
"stuff" objects. In this report, we successfully validated the effectiveness of
the decoupling strategy in video panoptic segmentation. Finally, our method
achieved a VPQ score of 51.4 and 53.7 in the development and test phases,
respectively, and ultimately ranked 1st in the VPS track of the 2nd PVUW
Challenge. The code is available at this https URL

本文介绍了一种基于解耦策略的视频全景分割方法，该方法在开发和测试阶段分别达到了 51.4 和 53.7 的 VPQ 得分，并在第二届 PVUW 挑战赛的 VPS 赛道上排名第一。