  We find Mask2Former also achieves state-of-the-art performance on video
instance segmentation without modifying the architecture, the loss or even the
training pipeline. In this report, we show universal image segmentation
architectures trivially generalize to video segmentation by directly predicting
3D segmentation volumes. Specifically, Mask2Former sets a new state-of-the-art
of 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021. We believe
Mask2Former is also capable of handling video semantic and panoptic
segmentation, given its versatility in image segmentation. We hope this will
make state-of-the-art video segmentation research more accessible and bring
more attention to designing universal image and video segmentation
architectures.
