  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.
