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.

为应对在野外进行视频全景分割任务，我们提出了一种强大的综合视频全景分割解决方案，通过将视频全景分割任务视为分割目标查询任务，利用神经网络提取的视频特征来预测分割掩码，同时添加了视频语义分割和视频实例分割等额外任务进行联合训练，提高了解决方案的学习准确性和收敛速度，最终在 VIPSeg 数据集上获得了第三名的好成绩。