  In order to deal with the task of video panoptic segmentation in the wild, we
propose a robust integrated video panoptic segmentation solution. In our
solution, we regard the video panoptic segmentation task as a segmentation
target querying task, represent both semantic and instance targets as a set of
queries, and then combine these queries with video features extracted by neural
networks to predict segmentation masks. In order to improve the learning
accuracy and convergence speed of the solution, we add additional tasks of
video semantic segmentation and video instance segmentation for joint training.
In addition, we also add an additional image semantic segmentation model to
further improve the performance of semantic classes. In addition, we also add
some additional operations to improve the robustness of the model. Extensive
experiments on the VIPSeg dataset show that the proposed solution achieves
state-of-the-art performance with 50.04\% VPQ on the VIPSeg test set, which is
3rd place on the video panoptic segmentation track of the PVUW Challenge 2023.
