  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 https://github.com/zhang-tao-whu/DVIS
