1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
This work addresses video panoptic segmentation for applications like video editing and autonomous driving, but it is incremental as it builds on an existing decoupling strategy.
The paper tackled video panoptic segmentation by validating a decoupling strategy, achieving VPQ scores of 51.4 and 53.7 in development and test phases, and ranking first in a challenge.
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