Mask2Former for Video Instance Segmentation
This makes state-of-the-art video segmentation more accessible for researchers and practitioners, though it is incremental as it applies an existing method to new data.
The paper demonstrates that Mask2Former, an image segmentation architecture, achieves state-of-the-art performance on video instance segmentation without modifications, achieving 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021.
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.