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Video instance segmentation aims at predicting object segmentation masks foreach frame, as well as associating the instances across multiple frames. Recentend-to-end video instance segmentation methods are capable of performing objectsegmentation and instance association together in a direct parallel sequencedecoding/prediction framework. Although these methods generally predict higherquality object segmentation masks, they can fail to associate instances inchallenging cases because they do not explicitly model the temporal instanceconsistency for adjacent frames. We propose a consistent end-to-end videoinstance segmentation framework with Inter-Frame Recurrent Attention to modelboth the temporal instance consistency for adjacent frames and the globaltemporal context. Our extensive experiments demonstrate that the Inter-FrameRecurrent Attention significantly improves temporal instance consistency whilemaintaining the quality of the object segmentation masks. Our model achievesstate-of-the-art accuracy on both YouTubeVIS-2019 (62.1\%) and YouTubeVIS-2021(54.7\%) datasets. In addition, quantitative and qualitative results show thatthe proposed methods predict more temporally consistent instance segmentationmasks.

Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention